Capability
AI & Automation
Most AI projects stall in the gap between a convincing demo and a system somebody trusts on a Tuesday afternoon. We work in that gap: models grounded in your own data, assistants scoped to one decision rather than all of them, and the governance review built into delivery instead of appended to it.
What we take on
Custom LLMs & RAG
Domain models grounded in your data
Agents & decision support
Assistants that act inside workflows
Process automation (RPA)
Rules and models for repetitive operations
Responsible AI reviews
Risk, bias and governance checks
Data & model pipelines
From raw data through to serving
AI readiness assessment
Where AI pays off first, and where it does not
Our stance
Where this helps today — and where it does not
Worth doing now
- Retrieval over the documents your team already argues about, where a wrong answer is visible and cheap
- Classification and extraction at volumes a person cannot sustain, with the error cost understood in advance
- Assistants scoped to a single workflow, with a human holding the decision that matters
- Drafting and summarising where a named person still signs the output
Stays classical, for now
- Decisions carrying regulatory accountability a model cannot hold
- Deterministic reporting — a query is cheaper, faster and auditable
- Anything where you cannot yet measure the baseline you intend to beat
- Processes still changing weekly; automate them once they settle, not before
