Services

LLM applications, built for production.

We build production LLM applications — chat, extraction, classification and drafting — wrapped in evaluation, guardrails and cost controls that hold up live.

A demo that works once is easy; an LLM feature that holds up under real users, real edge cases and a real budget is the hard part. We build language-model applications around the unglamorous engineering — prompts under version control, evaluation harnesses, fallbacks, cost controls and the option to run models on your own hardware — so the thing keeps working after launch day.

Where we help

Chat and assistant interfaces grounded in your own data and tools
Structured extraction and classification with schema-validated output
Drafting, summarisation and rewriting workflows with a human in the loop
Prompt engineering, versioning and regression-tested prompt suites
Evaluation harnesses that score accuracy, refusals and drift over time
Cost and latency controls — caching, routing, smaller models where they win
Local and on-premise models (Llama, Mistral) where data cannot leave your walls
Provider-agnostic wiring across Anthropic, OpenAI and open-weight models

Example builds

Local-LLM CRM. Our privacy-focused CRM runs language models entirely on-premises for non-profits and PACs — full AI assistance over donor and constituent records without sensitive data ever leaving the building.

Watchlist intelligence. StockTrack reads news articles and exchange announcements against each investor's watchlist, then summarises what actually matters — turning a firehose into a short, useful brief.

Beyond a single prompt. When one model call isn't enough, we compose agentic systems — specialist steps with their own tools, evaluators and a human checkpoint — to carry a whole task end to end.

How we engage

Most engagements open with a two-week sprint: we pick one workflow, build it against your real data, and put numbers on accuracy and cost before any wider rollout. From there a scoped build takes it to production with evaluation and monitoring wired in. We deliberately reach for the smallest model and simplest design that clears the bar. See our services for how we scope and price, or get in touch with the workflow you have in mind.

Common questions

Which model providers do you use?: We are provider-agnostic and routinely work across Anthropic Claude, OpenAI and open-weight models such as Llama and Mistral. We pick per workload and keep the wiring swappable so you are never locked to one vendor.
Can the model run on our own infrastructure?: Yes. We have shipped applications that run open-weight models entirely on-premises so no data leaves your environment — a common requirement for legal, health and political clients.
How do you stop the model making things up?: We ground answers in your data through retrieval, constrain outputs to validated schemas, and run an evaluation harness that scores accuracy and hallucination on every change. Where it matters, a person reviews before anything ships.

AI & machine learning Retrieval-augmented generation (RAG)MLOps & model deployment

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Where we help

Chat and assistant interfaces grounded in your own data and tools
Structured extraction and classification with schema-validated output
Drafting, summarisation and rewriting workflows with a human in the loop
Prompt engineering, versioning and regression-tested prompt suites
Evaluation harnesses that score accuracy, refusals and drift over time
Cost and latency controls — caching, routing, smaller models where they win
Local and on-premise models (Llama, Mistral) where data cannot leave your walls
Provider-agnostic wiring across Anthropic, OpenAI and open-weight models

Example builds

How we engage

Common questions

Which model providers do you use?

We are provider-agnostic and routinely work across Anthropic Claude, OpenAI and open-weight models such as Llama and Mistral. We pick per workload and keep the wiring swappable so you are never locked to one vendor.

Can the model run on our own infrastructure?

Yes. We have shipped applications that run open-weight models entirely on-premises so no data leaves your environment — a common requirement for legal, health and political clients.

How do you stop the model making things up?

We ground answers in your data through retrieval, constrain outputs to validated schemas, and run an evaluation harness that scores accuracy and hallucination on every change. Where it matters, a person reviews before anything ships.