AI Development
AI inside products that already have users, data and uptime to protect
AI is a component, not a product. We build LLM features, retrieval search over your own documents, document automation, computer vision and agents, and we wire them into systems that already run, with evaluation, fallbacks and cost controls in place before launch. Over 30 AI-based projects delivered to production.
WHAT THE ENGAGEMENT INCLUDES
- Feasibility review: what AI will and will not solve here
- Retrieval over your own documents, with citations and access control
- LLM features embedded in an existing product
- Evaluation harness so quality is measured, not assumed
- Fallbacks and cost controls before launch
- Ongoing tuning as real usage arrives
Questions we get asked
Can AI work with our own company data?
Yes. Retrieval-augmented generation indexes your documents so answers come from your material with citations, and access control decides who can see what. Your data is not used to train a public model.
How do you stop it inventing answers?
By measuring. We build an evaluation set from real questions, check answers against it, and add fallbacks so the system says it does not know rather than guessing. That work happens before launch, not after complaints.
What does it cost to run?
Model usage is a running cost, so we size it during the build and put controls in place: caching, smaller models where they suffice, and limits that stop a bug becoming a bill.