Applied AI that belongs inside a working system.
We integrate AI and machine-learning capabilities into products and workflows where they can reduce manual work, improve access to information or support better operational decisions.
Problems we solve
- Teams spending substantial time searching, summarising or routing unstructured information
- Products that need classification, extraction, prediction or natural-language interaction
- Internal knowledge that is difficult to retrieve consistently across teams
- AI prototypes that work in demonstrations but lack evaluation, guardrails or production integration
What we build
- LLM-powered product features
- Retrieval and document intelligence workflows
- Search and knowledge interfaces
- Classification and prediction services
- AI-assisted process automation
- Model and API integration
Engineering priorities
Evaluation
A representative test set and measurable success criteria come before claims about model quality.
Grounding
Where factual accuracy matters, retrieval and validation are designed around trusted source material.
Control
High-impact actions use permission boundaries, deterministic validation or human review where appropriate.
Economics
Latency and cost are treated as product constraints and measured per useful task, not in isolation.
Common questions
Do you recommend AI for every automation problem?
No. Rules, search, workflow design or conventional software are often cheaper and more predictable. We use AI when its probabilistic capability creates enough value to justify the additional complexity.
Can you integrate existing model providers?
Yes. The engineering problem often includes provider integration, retrieval, evaluation, permissions, monitoring and product workflow design rather than training a model from scratch.
How do you reduce unreliable model output?
The approach depends on the task, but usually combines better context, constrained outputs, validation, evaluation sets, permission boundaries and fallback or human-review paths.