Sheet · Governed AI
AI with guardrails, not magical promises
Assistants, agents, RAG and workflows only inside clear perimeters: boundaries, oversight, policy and output trails — aligned with systems, privacy and compliance.
Prototype versus production
Demos optimise for demos. Production cares about latency, cost, failure modes, abuse, data subject rights, and what happens when an output is unacceptable.
Responsible AI treats LLMs as probabilistic systems
Put boundaries around them, verify material decisions and keep humans in the loop where accountability must remain human.
What we integrate
Chatbots, agents, RAG, orchestration, MCP servers and automation, with a clear data, permission, retention and logging matrix.
AI security controls
Human-in-the-loop, instrumentation policy, versioning, audit trail, sampling, fallbacks, data minimisation and safe shutdowns.
Useful tools: AI risk & privacy checklist and AI Structure.
Value with a perimeter, not surprises in production
Share scope, data, users, limits and what your organisation accepts as proof. We define what can be automated, what must remain human and how to trace it.
Design a controlled AI use case