Enterprise AI architect · Context layers for AI agents · DACH
I help teams whose AI assistants already read chat, tickets, wiki and CRM answer from current, cited facts and take actions a person approves. I build the context layer on your own tenant, with delegated access, a temporal knowledge graph and an audit trail. We can start with one governed workflow; implementation is billed by the hour with an estimate for each phase.
Explore the work: 50-second demo and case study · Consulting approach · Discuss your workflow
Building this yourself? Follow this profile for implementation notes and open-source tools for governed AI systems.
The case study describes a production workflow. Its public demo and screenshots use fictional records to protect the client.
| If you need to… | See how I approach it |
|---|---|
| Find the current customer decision across disconnected systems | Operational context layer — sources, timestamps and permissions travel with each answer |
| Let an agent act without giving it unchecked write access | draftcat and agent-approval-gate — proposals, human verdicts and audited dispatch |
| Keep track of what was true when | graphiti-local — a local temporal knowledge graph with read-only MCP retrieval and human-gated fact ingestion |
- agentic-task-system connects existing task and knowledge tools to agent context, with provenance, reviewed writes and undo. The underlying systems remain the source of truth.
- skillgate runs deterministic completion checks for AI coding agents before commit or publication.
- My merged contributions include RAG query validation before logging in Tether's QVAC and GPT-5/o-series vision support in WeKnora. See all merged PRs.
I also build creator tooling: capcut-cli is an independent CLI for editing CapCut/JianYing project files.
I publish implementation notes for people building agents beyond demos. Recent examples:
- Permission-aware retrieval without count, title or provenance leaks
- Why an agent proposes facts but never writes directly to the knowledge graph
- Validate a RAG query before its first log line
I work with DACH teams that need AI to answer from their actual operational state and act within clear permissions. A first engagement can review one workflow's sources, identity, write path and failure modes before a phased build.
Discuss your workflow · Email me · Malt profile and references