“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)
Evidence for AI-generated code
Is your team shipping AI-generated code that has to hold up — in review, in front of a client, or in a SOC 2 audit? That's why I built Quoderat: an independent evidence audit of one risky or AI-generated GitLab merge request. You get a signed evidence report — what's proven, what's only tested, what isn't checked, and the residual risk.
AI that stays professional with sensitive input
What if the input isn't code, but the most personal thing someone has? That's why I built Dreamalizing: my own AI product that lets people explore their dreams — without interpreting as if it were the truth. Guided exploration instead of black-box output, clear limits (no therapy, no diagnosis), encrypted storage on our own infrastructure and local inference. It shows how AI can run bounded, privacy-first and in production even with sensitive personal input.