“Just add AI” sounds easy. In practice, many initiatives get stuck on quality, governance, and integration. Without context, measurability, and clear guardrails, AI quickly becomes an unreliable black box.
The problem
- Outputs vary because context is missing or changes over time
- Security and privacy are hard to guarantee (who can see what?)
- There’s no audit trail: why did the model answer this?
- Pilot success doesn’t translate to a stable production process
The solution
We bring AI to production by treating it as part of your system, not a standalone feature:
- Context (RAG) on your own sources: controlled and traceable
- Governance & access control: roles, logging, and policies
- Measurability: quality, cost, and latency as first-class metrics
- Integration: AI as a service embedded in existing workflows (APIs, UI, processes)