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For CX teams drowning in repetitive support tickets

An AI support brain that replies using your docs and resolved tickets

Reduce support workload and slow responses by 30–60%
without hiring, switching tools, or building chatbots.

Reduce support workload and slow responses by 30–60% without hiring, switching tools, or building chatbots.

See how it works

HOW IT WORKS

When a new ticket arrives, the AI pulls context from your help center, historical resolved tickets, and other integrated systems to create a response

WHAT’S INCLUDED

Everything your support team needs
to scale and work reliably

A single, custom, and focused system designed to reduce repetitive tickets, speed up replies, and keep answers consistent as support volume grows.

Other Integrated Tools

Internal Documentation

Historical Resolved Tickets

Help Center Documentation

Custom Knowledge Base

We connect your help center, resolved tickets, product docs, and FAQs so the AI always answers based on experience.

Other Integrated Tools

Internal Documentation

Historical Resolved Tickets

Help Center Documentation

Custom Knowledge Base

We connect your help center, resolved tickets, product docs, and FAQs so the AI always answers based on experience.

Other Integrated Tools

Internal Documentation

Historical Resolved Tickets

Help Center Documentation

Custom Knowledge Base

We connect your help center, resolved tickets, product docs, and FAQs so the AI always answers based on experience.

CRM & tool integrations

Connect your CRM, e-commerce platforms like Shopify, and customer communication tools to pull relevant customer context, order data, and history. This ensures each response is accurate, personalized, and grounded in the systems where your data already lives.

CRM & tool integrations

Integrated Into Helpdesk

Responses are generated directly where your team already works. Responses can be automatic, or with human-in-the-loop process

Integrated Into Helpdesk

Responses are generated directly where your team already works. Responses can be automatic, or with human-in-the-loop process

Integrated Into Helpdesk

Responses are generated directly where your team already works. Responses can be automatic, or with human-in-the-loop process

Resolved Tickets

+ Add

Resolved Ticket Context #23145

Resolved Ticket Context #23144

Resolved Ticket Context #23143

Ticket history learning

The AI learns from resolved tickets to understand how your team actually responds, not how documentation says it should.

Resolved Tickets

+ Add

Resolved Ticket Context #23145

Resolved Ticket Context #23144

Resolved Ticket Context #23143

Ticket history learning

The AI learns from resolved tickets to understand how your team actually responds, not how documentation says it should.

Resolved Tickets

+ Add

Resolved Ticket Context #23145

Resolved Ticket Context #23144

Resolved Ticket Context #23143

Ticket history learning

The AI learns from resolved tickets to understand how your team actually responds, not how documentation says it should.

Code

class SupportBrainTuner:

def __init__(self):

self.confidence_threshold = 0.82

self.feedback_log = []

self.knowledge_version = 1.0

def review_response(self, ai_confidence, agent_feedback):

self.feedback_log.append({

"confidence": ai_confidence,

"feedback": agent_feedback

})

if agent_feedback == "incorrect":

self._tighten_rules()

elif agent_feedback == "approved":

self._reinforce_patterns()

def _tighten_rules(self):

self.confidence_threshold += 0.02

self.knowledge_version += 0.1

def _reinforce_patterns(self):

self.knowledge_version += 0.05

def system_status(self):

return {

"confidence_threshold": self.confidence_threshold,

"knowledge_version": self.knowledge_version,

"feedback_samples": len(self.feedback_log)

}

Ongoing Improvements

We monitor performance, refine answers, and update the system on a monhtly basis as your products, FaQs, and support needs evolve.

Code

class SupportBrainTuner:

def __init__(self):

self.confidence_threshold = 0.82

self.feedback_log = []

self.knowledge_version = 1.0

def review_response(self, ai_confidence, agent_feedback):

self.feedback_log.append({

"confidence": ai_confidence,

"feedback": agent_feedback

})

if agent_feedback == "incorrect":

self._tighten_rules()

elif agent_feedback == "approved":

self._reinforce_patterns()

def _tighten_rules(self):

self.confidence_threshold += 0.02

self.knowledge_version += 0.1

def _reinforce_patterns(self):

self.knowledge_version += 0.05

def system_status(self):

return {

"confidence_threshold": self.confidence_threshold,

"knowledge_version": self.knowledge_version,

"feedback_samples": len(self.feedback_log)

}

Ongoing Improvements

We monitor performance, refine answers, and update the system on a monhtly basis as your products, FaQs, and support needs evolve.

Code

class SupportBrainTuner:

def __init__(self):

self.confidence_threshold = 0.82

self.feedback_log = []

self.knowledge_version = 1.0

def review_response(self, ai_confidence, agent_feedback):

self.feedback_log.append({

"confidence": ai_confidence,

"feedback": agent_feedback

})

if agent_feedback == "incorrect":

self._tighten_rules()

elif agent_feedback == "approved":

self._reinforce_patterns()

def _tighten_rules(self):

self.confidence_threshold += 0.02

self.knowledge_version += 0.1

def _reinforce_patterns(self):

self.knowledge_version += 0.05

def system_status(self):

return {

"confidence_threshold": self.confidence_threshold,

"knowledge_version": self.knowledge_version,

"feedback_samples": len(self.feedback_log)

}

Ongoing Improvements

We monitor performance, refine answers, and update the system on a monhtly basis as your products, FaQs, and support needs evolve.

WHO THIS IS FOR

Built for teams committed to
scaling support, not experiments

This works best for teams with real support volume and repetitive questions.
It’s not designed for one-off automations or custom builds without maintenance.

High-volume Support Teams

Teams handling dozens of tickets per day who need faster, more consistent responses without increasing headcount.

High-volume Support Teams

Teams handling dozens of tickets per day who need faster, more consistent responses without increasing headcount.

High-volume Support Teams

Teams handling dozens of tickets per day who need faster, more consistent responses without increasing headcount.

Established Knowledge Base

Companies with help centers, FAQs, policies, thousands of already resolved tickets or internal docs that should be actively used in every support reply.

Established Knowledge Base

Companies with help centers, FAQs, policies, thousands of already resolved tickets or internal docs that should be actively used in every support reply.

Established Knowledge Base

Companies with help centers, FAQs, policies, thousands of already resolved tickets or internal docs that should be actively used in every support reply.

Human-in-the-loop Flows

Teams that want AI-assisted draft replies reviewed by agents — or customer-facing chatbots sending answers automatically.

Human-in-the-loop Flows

Teams that want AI-assisted draft replies reviewed by agents — or customer-facing chatbots sending answers automatically.

Human-in-the-loop Flows

Teams that want AI-assisted draft replies reviewed by agents — or customer-facing chatbots sending answers automatically.

Handle Repetitive Questions

Businesses answering the same product, shipping, billing, or policy questions again and again across support channels.

Handle Repetitive Questions

Businesses answering the same product, shipping, billing, or policy questions again and again across support channels.

Handle Repetitive Questions

Businesses answering the same product, shipping, billing, or policy questions again and again across support channels.

Scaling Without Hiring

Teams looking to delay or reduce support hires by improving response speed and knowledge access instead of adding people.

Scaling Without Hiring

Teams looking to delay or reduce support hires by improving response speed and knowledge access instead of adding people.

Scaling Without Hiring

Teams looking to delay or reduce support hires by improving response speed and knowledge access instead of adding people.

Modern Support Stacks

Teams already using tools like HelpScout, Zendesk, Intercom, CRMs, or e-commerce platforms where customer data lives.

Modern Support Stacks

Teams already using tools like HelpScout, Zendesk, Intercom, CRMs, or e-commerce platforms where customer data lives.

Modern Support Stacks

Teams already using tools like HelpScout, Zendesk, Intercom, CRMs, or e-commerce platforms where customer data lives.

PROCESS

A simple, and scalable setup process

From your existing support data to live draft replies in days, not months. Without disrupting how your team works.

STEP 1

STEP 2

STEP 3

Discover & Connect Support Data

We connect your help center, FaQ, resolved tickets, and key systems like your CRM or e-commerce platform to create a single source of truth for support answers.

STEP 1

STEP 2

STEP 3

Discover & Connect Support Data

We connect your help center, FaQ, resolved tickets, and key systems like your CRM or e-commerce platform to create a single source of truth for support answers.

STEP 1

STEP 2

STEP 3

Discover & Connect Support Data

We connect your help center, FaQ, resolved tickets, and key systems like your CRM or e-commerce platform to create a single source of truth for support answers.

Your team stays in control. The responses stay accurate.

STUDY CASES

Success Stories to Inspire

Real support teams using the same system to reduce workload, improve response times, and scale without hiring.

Smarter Support Replies

The support team was spending most of their day answering the same product, shipping, and return questions. As order volume grew, response times slowed and hiring felt inevitable. We implemented an AI support brain trained on their help center and resolved tickets to draft replies directly inside their helpdesk.

%

Fewer repetitive tickets

%

Faster response time

Emily's E-commerce Success

Emily, the CEO of BloomTech, transformed their marketing efforts using AI-powered tools. This shift resulted in a 60% increase in ROI and a 45% improvement in customer personalization, leading to a surge in brand loyalty

%

growth in sales

%

rise in engagement

Sophia's Retail Breakthrough

Sophia, the marketing lead at Trendify, used AI-driven analytics to dive deep into customer behavior. The insights led to a 40% increase in engagement and a 30% rise in repeat purchases, creating long-term customer relationships.

%

gain in retention

%

surge in profits