Human-controlled AI for specialist artwork workflows
A provider-neutral advisory AI layer combined with deterministic reconstruction rules, immutable artifact lineage, and explicit human decisions at every production-sensitive boundary.
This entry does not name a real client. It is labelled “Capability demonstration” and should be read accordingly.
Business context
AI can accelerate artwork discovery and reconstruction work, but direct autonomous AI output would create quality, ownership, and production risk if left unchecked.
Problem
The business needed the speed benefit of AI-assisted discovery without exposing production decisions to unreviewed AI output.
Constraints
- No AI output could be used in production without human review
- The system needed to remain neutral to any single AI model provider
Existing systems
- Digital asset management
- Production workflow tools
What Anav did
- Built an AI-assisted discovery and interpretation layer
- Implemented a deterministic reconstruction workflow separate from AI-generated suggestions
- Established human review and sign-off at every production-sensitive decision point
- Built an immutable lineage record for every artifact touched by the workflow
Solution view
AI suggestions and deterministic reconstruction logic are kept architecturally separate, with a human decision point positioned wherever an artifact moves toward production use.
Human & governance controls
- AI output is always presented as a suggestion, never as an automatic production input
- Every artifact has an immutable lineage record covering AI involvement and human decisions
Delivery stages
- Architecture design separating AI-assisted and deterministic paths
- Capability demonstration built and reviewed internally
- Human-review workflow defined and tested
Outcomes
Working capability demonstration
Evidence source: Internal capability demonstration, not yet deployed to a live client
Technologies used
- Provider-neutral AI model integration
- Deterministic rules engine
- Immutable lineage store
What remains to be proven
- Production deployment and real-world throughput have not yet been measured
Bring us the operating problem.
Not a brief, not a platform shortlist — the decision that arrives late, the workflow held together by people, the AI question without a clear answer. That is enough to start.