Zyndix
Custom LLM · Built end to end by Zyndix

A support agent that learned from three years of real conversations.

For Mokslinės Leidybos Deimantas MB, a Lithuanian conference organizer, we built a private AI support agent — a custom LLM trained on years of real support history and connected to Chatwoot, so it answers customers instantly and escalates only what truly needs a human.

Get a free auditRunning live in their Chatwoot inbox
support inbox · Chatwoot
RK
Rasa K.
Conference registration
AI Agent
When is the deadline to submit my abstract for the spring conference?
The abstract deadline for the spring edition is March 14. You can submit and edit it any time before then from your registration dashboard — want the direct link?
ANSWERED BY AI · FROM SUPPORT HISTORY

Private note · Refund-policy question detected — escalated to a human with the full thread attached.

3 years

of real conversations, learned

Private

runs on infrastructure they control

Chatwoot

answering live in their inbox

01The challenge

The same questions, answered a thousand times — by a human, every time.

A conference organizer fields the same support questions endlessly — deadlines, payments, abstract status, registration details — across hundreds of events. The answers already existed, buried in three years of past conversations. But every question still landed on a human, who had to read, recall, and retype what the team had answered a thousand times before. And sending that history to a public AI tool wasn't an option: it's real customer data, and it had to stay private.

Before
  • Every question answered by a human, from scratch
  • Years of answers locked in past conversations
  • Slow replies, especially outside work hours
  • Customer data can't go to public AI tools
With the AI support agent
  • Routine questions answered instantly, automatically
  • That history is the agent's knowledge
  • Always-on, instant first response
  • Private model on infrastructure they control
02What we built

Their best answers, finally working on their own.

A private AI support agent, trained on the company's own conversation history and wired directly into Chatwoot — the inbox the team already uses. When a customer writes in, the agent understands the question, answers from real support knowledge, and handles it end to end. Anything it's unsure about, it escalates to a human with the full context attached — without a single record leaving the company's control.

Private modelGrounded in real historyLives in Chatwoot
How one message is handled
RK
Customer asks a question in Chatwoot
The agent retrieves from 3 years of history

Grounds its answer in how the team really replied — no guessing.

ResolvedAnswered instantly, in the customer's language.
EscalatedHanded to a human with full context attached.
Live in production

The real thing — answering a real question.

A real customer asks how to submit an abstract — and the assistant answers instantly inside the live Chatwoot widget, grounded in the team's own support history.

Conference Assistant answering an abstract-submission question in the live Chatwoot widget

Actual conversation from the live assistant · powered by Chatwoot

03How it works · the agent pipeline

From three years of history to an answer in the inbox — in five stages.

01 · LEARN

Train on real history

Three years of real support conversations become the agent's knowledge base (RAG).

02 · GROUND

Answer only from truth

The model answers only from that history, with anti-hallucination rules — no guessing.

03 · CONNECT

Wire into Chatwoot

Connected to Chatwoot, so it works inside the inbox the team already runs.

04 · ANSWER

Handle it automatically

Customer questions handled instantly, in natural language, day or night.

05 · ESCALATE

Know when to step back

Anything uncertain is handed to a human — with full context, not a cold transfer.

04What makes it different

Private by design

Built on an open model and infrastructure the company controls. Customer data never leaves their hands or trains a third party's product.

Grounded in real history

Retrieval over three years of actual conversations means answers match how the team really responds, not a generic script.

Lives in Chatwoot

No new tool to learn — the agent works inside the inbox the team already uses every day.

Knows when to step back

Clear guardrails decide what it answers and what it escalates, with full context handed to a human.

Always on

Instant first response, day or night, in the customer's own language.

Things a generic AI vendor can't claim — private control of the data, and a real integration into the tool you already run.

05By the numbers
3 yrs

of conversations as training knowledge

Private

open model on infra they control

RAG

retrieval-grounded, anti-hallucination

Chatwoot

integrated into the live inbox

24/7

instant first response, always on

“Their best support answers, given over three years, finally working for them automatically — without a single record leaving their control.
06 · The stack
Custom LLM (open model)RAG / vector retrievalAnti-hallucination guardrailsChatwootPrivate infrastructure

A private, grounded support agent — designed, trained, and integrated end to end by Zyndix.

Free 30-minute audit

Sitting on years of support history?

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