AI features your users
can rely on, not just demo.
From prototype to production: AI built into real products, tested and monitored.
Why it
matters.
An AI prototype can impress in a week. Getting it to answer correctly for thousands of real users, at a sensible cost, takes engineering. We build AI into your product with evaluation sets, guardrails and monitoring from day one, so you know how well it works before users find out.
What
we do.
- 01
AI assistants and copilots inside your product
- 02
Search and question answering over your own documents
- 03
Document extraction, classification and summarisation
- 04
Agents that complete multi-step tasks with your tools
- 05
Evaluation sets, guardrails and human review steps
- 06
Cost, latency and quality monitoring in production
What you
get.
Deliverables
- A production AI feature, integrated into your product
- An evaluation set and quality scorecard
- Guardrails, fallbacks and human review flows
- Dashboards for quality, cost and latency
- Documentation for your engineers to extend it
Tools we work with
- Claude
- OpenAI
- Gemini
- Llama
- LangChain
- LlamaIndex
- pgvector
- Pinecone
- Python
- TypeScript
How we
work.
- 01
Define what a good answer looks like
We collect real examples from your users and agree, with you, how each should be answered.
- 02
Test against real cases before every release
Every prompt or model change is scored against that evaluation set, so quality only moves forward.
- 03
Watch quality and cost once it is live
In production we track accuracy, cost and response time, and route uncertain cases to a person.
Good fit
if…
You have an AI prototype that works in demos but not reliably for users, or want to add AI to a product without risking its reputation.
Common
questions.
Which AI model will you use?
The one that fits your accuracy, cost and privacy needs. We test a few on your own examples and keep the design flexible enough to switch later.
Will our data be used to train public models?
No. We use providers and settings that keep your data out of training, and can run open models on your own infrastructure when needed.
How do you stop the AI giving wrong answers?
We ground answers in your own data, test against an evaluation set before release, add guardrails, and send low-confidence cases to a person.
Ready when
you are.
Tell us what you’re working on. We’ll reply within one business day.
Talk to us about your AI product