Your data.
Your perimeter.
Your LLM.
Private large language models deployed on your infrastructure, and RAG systems that let your teams query internal documents in natural language, with zero data leaving your environment.
Ask AI about this service
Large language models are genuinely useful for enterprise knowledge management, document intelligence, and internal decision support. The problem is that most LLM products require your data to leave your environment. We deploy private LLMs on-premises or in your VPC, fine-tuned on your data, with zero public AI exposure. This capability sits at the core of our QWRY.AI platform and is available as a standalone engineering service for organisations that need the AI capability without the full platform.
What we build.
Private LLM Deployment
We deploy open-weight LLMs (Llama, Mistral, Qwen, and others) on your own servers or private VPC, so inference, fine-tuning, and serving stay fully contained, nothing touches a third-party cloud.
RAG Architecture
Retrieval-Augmented Generation lets your teams query SOPs, manuals, reports, and databases in natural language, with answers grounded in your actual documents and backed by citations.
Fine-Tuning
We adapt the model to your data and tasks, improving accuracy on domain queries, reducing hallucination, and aligning outputs to your terminology, taxonomy, and requirements.
Document Intelligence
We build pipelines that ingest and parse PDF manuals, scanned circulars, and legacy repositories, extracting structured information and making that knowledge queryable through LLM interfaces.
Answers from your documents within 7 days.
From the day you engage us, not from a kickoff weeks later. Every milestone is a result you can see.
- Day 2
Deployment model fixed: on-prem, private VPC, or air-gapped
- Day 7
Private LLM answering over a first document set, with citations
- Day 30
Production RAG with access control, evaluation, and monitoring
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Fixed scope
Scope, price, and milestones are agreed before work starts.
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No surprises
Weekly demos and plain-language status. Changes are priced before they are built.
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Compliance from day one
Nothing leaves your perimeter. Access control, retention rules, and audit logging are in place before the first document is ingested.
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You own the IP
Code, models, and documentation transfer to you on successful handover.
Know the price, the date,
and the exit before we start.
Fixed-scope project. A defined deliverable at a fixed price and date. Payment is released as each milestone is accepted. The same terms are written into every statement of work for private llm & rag, so there is nothing to discover later.
- Quote Within 5 working days Price per milestone, dates, and named architect
- Priced One price per milestone Third-party costs listed separately, at cost. Invoiced in USD for international clients
- Paid On accepted working software Not on calendar dates or hours logged
- Changes Written and priced first Cost and date impact before work starts
You fund accepted work, never a promise.
Drag to move through the engagement. Money is released only as each gate is accepted, so what you have paid never runs ahead of what you can use.
- Milestone 1Day 2
Deployment model fixed: on-prem, private VPC, or air-gapped
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 20% - Milestone 2Day 7
Private LLM answering over a first document set, with citations
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - Milestone 3Day 30
Production RAG with access control, evaluation, and monitoring
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - HandoverAt final acceptance
Code, architecture documents, deployment scripts, and credentials transferred to you.
Accepted whenFinal acceptance signed. Defects then fixed under warranty at no charge.
Releases 20%
Illustrative split. The exact percentages and dates are fixed in your statement of work.
When something goes wrong, who pays?
Every project meets surprises. What matters is who carries them. Pick one and compare how three commercial models respond.
The hours keep running while the team investigates. You pay for the surprise.
A change request is raised, usually priced at a premium and argued over.
We verified it before quoting, and the risk register held an allowance. Any overrun beyond it is ours.
It is absorbed into the hours. The date slips quietly and you learn at the invoice.
A change fee arrives, often as a lump sum with little explanation.
You get a written amendment with cost and date impact. Nothing starts until you approve it.
You pay for the difference. The estimate was only ever a guess.
The vendor reopens scope to recover margin, or cuts corners to hold it.
The price stays fixed. The overrun is ours to carry, and you see the budget position at every demo.
It is billed on the calendar anyway. The status report says "on track".
The invoice follows the date in the contract, whether or not the work is done.
Payment for that milestone is held until it passes the written criteria. You do not fund a miss.
One price, built in the open.
The proposal itemises every feature or milestone, names its assumptions, and keeps a risk register with allowances. Allowances you never use are not billed.
- Data readiness and volume
- Accuracy or quality bar to be met
- Deployment: on-prem, private VPC, or air-gapped
- Number of workflows or use cases
A representative data sample is available in week one.
If falseWe re-baseline the accuracy target and price together, in writing.
The quality bar is a number agreed up front, measured on your data.
If falseMoving the bar is a costed change request, not a quiet scope drift.
Hardware for private deployment is provisioned by Day 7.
If falseDates move by the delay. The price does not change.
- Cloud hosting
- Third-party licences
- Model and API usage
Invoiced in USD for international clients, with the currency fixed in the SOW. You own it. Code, models, documents, and credentials transfer on payment.
Built on a production-grade stack.
We pick tools proven in production and maintainable over long lifecycles - not whatever is trending. These are a few we reach for here; explore the full stack behind our engineering.
Explore our technology stack →
Articles on Private LLM & RAG
All articles→Private LLM, Enterprise API or On-Premises Model? Choosing the Right Deployment Architecture
Compare managed model APIs, private cloud and on-premises LLM deployment across data controls, quality, latency, operating cost and team capability.
Read article →Why an Enterprise AI Assistant Needs More Than Access to Your Documents
Understand the retrieval, permissions, freshness, evaluation and audit controls an enterprise AI assistant needs to answer reliably.
Read article →How to Make AI-Generated Business Answers Verifiable
Make natural-language analytics auditable with defined metrics, authorised source data, reproducible queries, lineage, limitations and evidence.
Read article →What buyers ask before they commit.
Straight answers on timelines, price, risk, and ownership, the same terms that appear in every proposal.
01What is Private LLM & RAG?
Private large language model deployment and Retrieval-Augmented Generation systems. On-premises or private VPC, your data never leaves your environment.
02How quickly does Private LLM & RAG deliver a first result?
Answers from your documents within 7 days. Typical milestones are Day 2: deployment model fixed: on-prem, private VPC, or air-gapped; Day 7: private LLM answering over a first document set, with citations; Day 30: production RAG with access control, evaluation, and monitoring. Exact dates are written into the statement of work before you commit.
03How is Private LLM & RAG priced?
Fixed-scope project: one price per milestone, with a first-release date, itemised in a statement of work within five working days of scoping. Payment is released as each milestone is accepted against written criteria. Cloud hosting, licences, and model usage are passed through at cost. International clients are invoiced in USD. The main price drivers are data readiness and volume, accuracy or quality bar to be met, deployment: on-prem, private VPC, or air-gapped, number of workflows or use cases.
04What happens if the scope changes or the work overruns?
The price stays fixed. A change to scope is quoted in writing, with its cost and date impact, and starts only after you approve it. If our own estimate was wrong, the overrun is ours, not yours. You see the budget position against milestones at every demo.
05Who owns the code, models, and documentation?
You do. Code, models, and documentation transfer to you on successful handover. Code, architecture documents, deployment scripts, and credentials transfer on payment, with no licence back.
06How are security and compliance handled?
Nothing leaves your perimeter. Access control, retention rules, and audit logging are in place before the first document is ingested.
07Do you work with clients outside India?
Yes. We are based in Noida and work with clients around the world, delivering remotely. Calls and demos are scheduled in your time zone, proposals and contracts are in English, international clients are invoiced in USD, and every engagement follows the same fixed-scope terms regardless of where you are.
08When would you decline to fix a price?
Open-ended research has no end date, so we time-box it. We commit a fixed fee to reach a measured baseline, and decide the next step with you from the evidence.
AI on your infrastructure, not someone else's.
Whether you want to deploy a private LLM, build a RAG system over your document repositories, or explore what QWRY.AI can do for your data environment, the conversation starts with understanding your data and your deployment constraints.