/ AI engineering
AI that does real work, not just demos
We design and build AI agents, LLM-powered features and automations that fit into how your business actually runs, with evaluation and guardrails built in.
/ who it is for
Built for teams like yours
Operations leads who want repetitive work automated
Founders building an AI-first product
/ what you get
What we deliver
AI agents and assistants
Agents that use your tools and data to complete multi-step tasks, with a human in the loop where it matters.
LLM integration
Clean, observable integrations with leading models, including retrieval over your own documents.
Workflow automation
Replace manual hand-offs with automations that log what they did and why.
Evaluation and safeguards
Test sets, monitoring and fallbacks, so quality is measured rather than assumed.
/ how it works
From first call to finished
- 01 Find the use case We look for where AI will genuinely save time or unlock value.
- 02 Prototype A small working version to test with real data and real users.
- 03 Harden Evaluation, guardrails, security and cost controls.
- 04 Launch and tune Release, observe and improve with real usage.
/ Tools we work with
Tools we work with
Claude, GPT and open models
Retrieval and vector search
Tool calling and agent frameworks
Python and TypeScript
/ questions
Good questions, plain answers
Which AI models do you use?
We choose per task. That might be Claude, GPT or an open model, depending on quality, cost, privacy and latency needs.
Can the AI work with our private data?
Yes. We design how data is accessed and what is sent to models, so it fits your privacy and compliance requirements.
How do you stop an agent doing something wrong?
Scoped permissions, approval steps for risky actions, logging, and tests that catch regressions.
/ next step
Let's talk about ai engineering
Tell us what you are working on. We will reply by email with questions and a suggested way forward.