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
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 knowledge
On-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 · cited
94% 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
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