Large language models your business can actually trust
Most organisations already use AI chatbots, often on personal accounts and with company data. The question is not whether to use LLMs, but how: which model, where it runs, what data it may see, and how you know the answers are good enough. We help you decide, then build it.
Why LLM projects stall
Staff paste data into public tools
Without an approved option, people use whatever is free. Customer data and internal documents leave the company without anyone deciding they should.
The pilot impressed, production did not
A demo on ten hand-picked questions looks great. Real users ask messier questions, and nobody measured quality before go-live.
The bill arrived
Usage grew, every request sent long documents to an expensive model, and monthly API cost became a board-level question.
Fine-tuning that did not help
Months went into fine-tuning a model to "know" company facts, when the real need was to look them up. See when fine-tuning helps.
Three ways to use an LLM
| Option | What it means | Good for | Watch out for |
|---|---|---|---|
| Public AI service | Calling a commercial model over the internet | Fast start, strongest models, no infrastructure | Where data goes, contract terms, cost at high volume |
| Private cloud endpoint | The same commercial models deployed in your own cloud account and region | Data stays in your cloud tenancy, enterprise contracts | Which models are available in which region; still per-token pricing |
| Self-hosted open model | An open-weight model running on your own GPUs | Strict data rules, steady high volume, full control over versions | You run the GPUs and the model platform; open models may trail the best commercial ones |
Many organisations use more than one: a self-hosted model for sensitive data, and a commercial API for everything else, behind one gateway.
Hosted API or self-hosted: what will it cost?
Enter your expected usage. The calculator compares a month of API charges with the cost of running an open model on your own GPUs, and checks whether those GPUs can keep up at the busiest hour.
Guides and examples
Choosing a model
Open or commercial, large or small, and how to test models on your own tasks.
Read the guide →Private hosting
Running open models inside your network: gateway, guardrails, model servers.
Read the guide →Fine-tuning
When it helps, when retrieval is the better answer, and what data you need.
Read the guide →Governance and LLMOps
Evaluation, guardrails, logging, access and data protection.
Read the guide →Use case: claims summaries
An illustrative scenario: an insurer summarising claim files with a private model.
Read the use case →LLM questions, answered
Data safety, cost, Indian languages, hallucinations and more.
Read the FAQ →Related: RAG to answer from your own documents, agentic AI to take actions, and GPU infrastructure to run it all. Full reference: LLM whitepaper.
How an LLM engagement runs
- Pick one task. A specific job with a clear owner, such as summarising claim files or drafting replies. Not "use AI everywhere".
- Build a test set. 100 to 300 real examples with good answers, agreed with the people who do the work today.
- Compare options. Two or three models, hosted and self-hosted, scored on the test set for quality, speed and cost.
- Build with guardrails. Gateway, access control, logging and data masking from day one.
- Pilot with real users. Measure quality and time saved against the baseline, and fix what users report.
- Run it. Monitoring, regular re-testing, and a process for changing models safely.
Starting with LLMs, or stuck after a pilot?
Tell us the task you want AI to help with and any rules about where your data can go. We will come back with a plain recommendation: which kind of model, where to run it, roughly what it costs, and how to know if it is working.