---
title: Custom LLM — Domain-tuned Enterprise LLMs | Zentavor
description: Domain-tuned LLMs and assistants grounded in your data: accurate, on-brand, without hallucinations.
canonical: https://zentavor.com/solution-custom-llm.html
---

# Custom LLM — Domain-tuned Enterprise LLMs | Zentavor

> Domain-tuned LLMs and assistants grounded in your data: accurate, on-brand, without hallucinations.

Solutions / Custom LLM

LLMs tuned to your domain. Grounded, not guessing

Domain-tuned models and assistants grounded in your data and policies: accurate, on-brand, deployable on-prem
Azure OpenAILoRA / PEFTRAG + citationsOn-premvLLM
9:41
Domain Assistantgrounded in your knowledgeOn-premise
Enterprise knowledge · in production
Grounded answer
What's our refund window for annual plans?
Pro-rata within 30 days, then creditscited
Sources
Billing Policy v4 · §7.20.94
Support Playbook0.88
grounded · cited94% accuracy
94%answer accuracy
90%query accuracy
−70%analyst load
What we build

## Models that speak your business

We don't bolt a chatbot onto a generic model. We tune it on your corpora, ground every answer in your sources, and gate releases behind an evaluation harness
Fine-tune

Teach it your domain

PEFT / LoRA on your corpora so the model knows your products, terminology and tone.
policy & product corpus
LoRA adapters
your terminology
Ground

Answer from your sources

RAG over enterprise knowledge: every claim cited, no source no answer.
retrieve · top-k[3]
Billing Policy v4 · §7.20.94
grounded answercited ✓
Eval harness

Gate every release

Automated accuracy, safety and regression checks before anything ships.
accuracy94% ✓
safety & policypass ✓
regression suite0 fails ✓
Deployment

## Runs inside your business

Azure OpenAI or open-weight models, served behind your own gateway: your data never leaves your environment

On-prem & private cloud

Open-weight models served on vLLM inside your network, or Azure OpenAI in your tenant.

Guardrails & policy

Tone, redaction and policy constraints enforced at the gateway and tested in the harness.

Data residency

Nothing leaves your perimeter. Meet residency and compliance rules without public APIs.

Monitoring & evals

Continuous quality and drift monitoring, automated eval runs against a regression set, and alerting when answers slip.
The challenge

## Generic models, generic answers

Off-the-shelf LLM

Foundation models lack your domain knowledge and terminology.
Hallucinations make unsupervised use too risky.
Tone and policy compliance aren't guaranteed.
Data-residency rules limit public-API use.

With Zentavor

Domain fine-tuning (PEFT/LoRA) on your corpora teaches your products and terms.
Grounded answers with citations + an eval harness: 94% answer accuracy.
Policy guardrails and tone constraints enforced and tested.
On-prem / in-perimeter deployment (Azure OpenAI or open-weight).
Proof

## Results in production

Data AccessLLM
90%
query accuracy
TextToSQL assistant grounded in your schema: analysts ask in plain language, get correct SQL back.
In productionNDA
Data AccessLLM
−70%
analyst load
Self-serve answers from the same assistant free analysts from routine query-writing.
In productionNDA
SupportGenAI
−1/3
cost per ticket
Support GenAI grounded in your policies: grounded, cited answers cut handling cost per ticket.
In productionNDA
Selected case studies available under NDA. Contact us for examples in your industry.
FAQ

## Your frequently asked questions

**
Do you fine-tune or just use RAG?**

Usually both. RAG grounds answers in live sources with citations; domain fine-tuning (PEFT/LoRA) teaches the model your terminology and tone. We scope the mix to your use case and data.

**
How do you stop hallucinations?**

Answers are grounded in retrieved sources and cited: no source, no answer. An evaluation harness checks accuracy and safety before every release, so unsupported claims are caught before they ship.

**
Can it run inside our perimeter?**

Yes. We deploy open-weight models on vLLM inside your network, or Azure OpenAI in your own tenant. Your data and prompts never leave your environment.

**
How do you measure accuracy?**

We build a task-specific eval set with your experts and track answer accuracy, citation correctness and regression on every change. In production assistants we typically reach ~94% answer accuracy.

**
What data do you need to start?**

Your knowledge sources (docs, wikis, tickets, schemas) and a handful of example questions with good answers. That's enough to stand up a grounded assistant and an eval baseline.

**
How do you handle policy and tone?**

Guardrails enforce tone, redaction and policy constraints at the gateway, and the eval harness tests them on every release, so compliance is verified, not assumed.
Let's talk

## An LLM that actually knows your business

Tell us the use case and data: we'll recommend tuning, grounding and a deployment model
Request a demoAll solutions →