What is a custom LLM?

A custom LLM is a large language model adapted to your business — connected to your knowledge through retrieval (RAG), fine-tuning, or both — so it answers accurately from your data instead of relying only on its general training.

What's the difference between RAG and fine-tuning?

RAG (retrieval-augmented generation) lets the model pull answers live from your documents at the moment it responds — great for accuracy and keeping answers current. Fine-tuning adapts the model itself to your domain and tone. We often combine them: RAG for facts, fine-tuning for depth and style.

Will my data stay private?

Yes. We can build on open models and deploy on infrastructure you control, so your data never leaves your environment or trains a third party's product.

Why not just use ChatGPT?

Public ChatGPT doesn't know your business and isn't private. A custom LLM answers from your specific knowledge, in your terminology, on infrastructure you control — which matters the moment accuracy or confidentiality does.

Do I need a custom LLM, or just a chatbot?

The LLM is the brain; the chatbot or agent is the face. If you want a smart assistant, start there — and a custom model underneath makes it genuinely expert in your domain.

How much does custom LLM development cost?

It depends on your data, accuracy needs, and whether you need private hosting. We scope it in the free audit and give you a fixed price before you commit.

Custom LLM Models

An AI model that actually knows your business.

Off-the-shelf AI knows the internet. It doesn't know your products, your policies, your history, or your customers. We build custom LLM models trained on your own knowledge — using retrieval (RAG) and fine-tuning — for accurate, private, on-brand answers grounded in your business, not generic guesses.

Get a free audit See how it works

We built a private RAG model trained on three years of a company's real support history — on an open model, on infrastructure they control.

Your Custom LLM

grounded in your knowledge

Private

What's our refund window for enterprise plans?

Searching your knowledge…

Refund Policy v3Enterprise T&CsBilling FAQ

Enterprise plans can be refunded within 45 days of the invoice date. After that, credits apply instead.

Source: Refund Policy v3, §2

What a custom LLM actually is

A capable model, connected to your knowledge.

Public AI is brilliant at the world's knowledge and useless on yours — it has never seen your documents. We close that gap in one of two ways. Often both.

Retrieval (RAG)

The model pulls answers live from your documents and data at the moment it responds. Best for accuracy, citeable answers, and knowledge that changes.

Always currentCites its source

Fine-tuning

We adapt the model itself to your domain and tone, so it answers like an expert in your field — not a generalist. Best for depth and voice.

Domain depthOn-brand tone

How it answers

It looks it up before it answers.

Every answer is grounded in your real documents — retrieved, then written — so it's accurate and you can see where it came from.

Ask

A real question, in plain language — from a person, your chatbot, or an app.

Retrieve

A vector search scans your knowledge base for the most relevant passages.

pgvector

Ground

The top passages are handed to the model as context — with strict rules to answer only from them.

Answer

A grounded, on-brand answer — with the source it came from, so you can trust it.

Your knowledge base feeds step 2:

Documents & manualsSupport historyPolicies & FAQsProduct data

See the difference

Same question. Very different answers.

A customer asks: "What's the refund window on my enterprise plan?"

Generic public AI

"I don't have access to your company's specific policies, but refund windows are typically 14–30 days. Please check your terms or contact support."

✕Guesses from the open internet

✕No source — can't be trusted

✕Sends the customer away

Your custom LLM

"Enterprise plans can be refunded within 45 days of the invoice date; after that, account credit applies."

Source: Refund Policy v3, §2

✓Answers from your real policy

✓Cites the exact source

✓Private — data never leaves you

What you get

An AI brain, built around your business.

Answers grounded in your data

It responds from your documents, history, and knowledge — not the open internet — so it's accurate to your business.

Private and under your control

We can deploy on open models and infrastructure you own, so your data never leaves your hands or trains someone else's product.

RAG + fine-tuning

We pick the right approach: retrieval for live, citeable answers; fine-tuning for domain depth and tone. Often a blend of both.

Speaks your domain

Trained on your terminology, products, and policies, it answers like an expert in your field — not a generalist.

Stays current

As your knowledge changes, the model's sources update — so its answers don't go stale.

Built to plug in

The model becomes the brain behind your chatbot, support agent, internal search, or app.

How we build it

From your knowledge to a model you trust.

Map the knowledge

In the free audit we identify what the model needs to know — and what "a good answer" looks like for you.

Build the model

We set up retrieval, fine-tune where it adds value, and add strict anti-hallucination rules so it answers from your sources.

Deploy privately

We can run it on open models and infrastructure you control — so your data stays yours.

Connect & maintain

We wire the model into your chatbot, agent, or app, and keep its knowledge fresh as your business changes.

Who it's for

When generic AI isn't enough.

Teams sitting on years of knowledge

Support histories, documentation, manuals — a custom LLM turns that pile into an instant, accurate answer engine.

Businesses that can't send data to public AI

If privacy or compliance rules out pasting your data into ChatGPT, we build a private model on infrastructure you control.

Anyone building an AI product

If you need an AI feature that's accurate to a specific domain, the custom model is the foundation it stands on.

We've built this for real

A private support model — RAG over three years of real conversation history.

For Acavent, we built a private support model using retrieval over three years of real customer conversations, running on an open model the company controls — so it answers from their actual support knowledge without sending a word to a third-party AI. That's custom LLM development in production: accurate, private, grounded in real business data.

3 yrs

of support history indexed

Open

model, on infra they control

data sent to third-party AI

Read the case study

The tech we use

We choose the model and setup that fit your accuracy, privacy, and budget — not a one-size default.

ClaudeGPT-4Open models (Llama, Phi)Supabase / pgvectorYour own infrastructure

Questions

Give your business an AI that actually knows it.

Book a free 30-minute audit. We'll find where a custom model would make AI accurate and private enough to trust — before you spend a cent.

Get a free auditNo obligation, no spam.