AI engineering
AI features that do real work, and show you how well.
We integrate large language models into products and internal tools: document assistants, retrieval-augmented search, structured extraction and more, with evaluation so you can trust the results.
The problem
A demo is easy. Dependable is hard.
A prototype chatbot can be built in an afternoon. Making it answer from your own documents, cite its sources, refuse when it doesn't know and stay affordable at scale is the real work.
We treat AI features like any other software: defined behaviour, tests, monitoring, and a clear view of cost and failure modes.
Our approach
How we tackle it.
Pick problems where AI fits
Summarising, searching, drafting and extracting are strong fits. We'll be honest when a rule or a form would do better.
Ground answers in your data
Retrieval-augmented generation (RAG) lets the model answer from your documents, with sources shown to the user.
Evaluate before and after launch
We build a test set of real questions and score answers, so changes to prompts or models are measured, not guessed.
Keep data and costs in check
Careful handling of what is sent to model providers, plus caching and model choice to control spend.
What you get
Deliverables
- Use-case assessment and success criteria
- LLM integration in your product or tool
- Retrieval pipeline over your documents (RAG)
- Evaluation set and quality reports
- Guardrails, logging and cost monitoring
- Production deployment
Where it helps
Use cases
- Document assistants
- Ask questions of policies, manuals or contracts and get answers with links to the source.
- Search that understands intent
- Semantic search across a knowledge base, tickets or product catalogue.
- Structured extraction
- Turning invoices, forms or emails into clean, structured data.
- Drafting help
- First drafts of replies, summaries or reports for a person to review and send.
Technical capabilities
- LLM APIs (Claude, OpenAI and others)
- Retrieval-augmented generation
- Embeddings
- pgvector
- Python
- FastAPI
- Prompt and output evaluation
- Structured outputs
Delivery
What working with us looks like.
- 1
Define success
Agree what a good answer looks like and collect real example questions.
- 2
Prototype and measure
A working prototype scored against the example set.
- 3
Harden
Guardrails, source citations, fallbacks and cost controls.
- 4
Deploy and monitor
Release to users, with quality and cost tracked over time.
Questions
Common questions.
We work with leading LLM providers, including Anthropic's Claude and OpenAI models, and choose based on quality, cost and data requirements for your use case.
Related services
Let's talk about your project.
Share what you're trying to achieve. You'll hear back from an engineer, not a sales script.