Generative AI & LLM Engineering
A prompt in a notebook is not a product. Turning a language model into something a business can rely on means evaluation you trust, prompts under version control, a cost model that does not surprise finance, and a plan for the day the model returns something wrong. We build LLM applications end to end — model selection, retrieval, structured output, guardrails, evaluation harnesses and the observability to see what users actually asked for.
How we work on this
- Use-case assessment — what LLMs can and cannot do for your workflow
- Pilot — one workflow, properly evaluated, with a cost model
- Production build — guardrails, observability, evaluation in CI
- Model operations — ongoing evaluation as models and prompts change
Our Capabilities
Comprehensive Generative AI capabilities backed by deep expertise and proven methodologies.
Technologies We Use
We leverage industry-leading tools and frameworks to build robust, scalable solutions.
What we would tell you in the first meeting
The teams getting value from language models are not the ones with the cleverest prompts. They are the ones who built an evaluation set early, so they can tell whether a change made things better or worse. Everyone else is guessing confidently. Start with how you will measure it, before you pick a model.
Generative AI — Frequently Asked Questions
How do you stop an LLM application from making things up?+
Three things, in order of impact. First, ground it: retrieval from your own documents so the model answers from source material rather than memory. Second, constrain it: structured output and validation, so a malformed or unsupported answer fails rather than reaches the user. Third, measure it: a task-specific evaluation set that runs on every prompt or model change, so you find regressions before your customers do. No model choice substitutes for those three.
Which model should we use?+
It depends on the task, and the honest way to find out is to benchmark candidates on your own data rather than trust a public leaderboard. We usually test two or three across the workloads you actually run, comparing quality, latency and cost per request. Many production systems end up routing between models — a cheap one for the common case, a stronger one for the hard case — because that is where the economics work.
What does an LLM application cost to run?+
The model bill is usually the smaller half. Cost is driven by how much context you send on every request, how often you retry, and whether you cache. We build a cost model during the pilot so you know the per-request and monthly figure before committing, and we design for it — context discipline, caching and routing — rather than discovering it on an invoice.
Products built on this work
Software we run in production, not a slide about someone else's project.
All productsRelated Services
Ready to Get Started with Generative AI?
Tell us what you are trying to build and what is in your way. We will tell you how we would approach it — including when the answer is that you do not need us.
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