AI & agentic systems
Agents and retrieval that do a defined job inside your product, with evals to prove it.
Our approach
Most AI projects stall between the demo and production. We start from the job the system has to do, decide whether a model is the right tool for it, and build the retrieval, guardrails and evaluation around it so you can measure whether it works before your customers do.
What it covers
- AI agents and orchestration
- RAG and enterprise knowledge systems
- LLM integrations
- AI workflow automation
- Evaluation and guardrails
What you get
- A written evaluation set built from your real data, run on every change
- Retrieval pipeline over your documents with source citations in every answer
- Provider-agnostic model layer, so you can switch models without a rewrite
- Cost, latency and failure dashboards from day one
A good fit when
- Support or sales teams answering the same questions from scattered docs
- Back-office work that follows rules but still needs judgement
- A product feature where a model could remove several steps for the user
Usually built with
- OpenAI API
- LangGraph
- Hugging Face
- pgvector
- Python
- FastAPI
Questions people ask first
Ask something elseDo we need to train our own model?
Almost never. Most business problems are solved with a hosted model, good retrieval over your own data, and careful prompts and tooling. We will tell you if fine-tuning is genuinely warranted.
What happens to our data?
Data stays in infrastructure you control. We use providers' zero-retention options where available and document exactly what leaves your environment.
How do you know it works?
Before building, we agree on a test set and pass criteria with you. Every release is scored against it, so quality is a number you can track rather than a feeling.
Related concept work
Have an ambitious idea? Let's engineer it.
Tell us what you're trying to do. A senior engineer reads every message and replies personally.