An intelligence layer that fits into your ecosystem.
Chaukas is designed to sit beside the systems that already record, schedule and support mission-critical assets — and to give manufacturers, integrators and technology partners a controlled way to make their knowledge useful at the point of use.
Complementary by design.
Chaukas does not replace ERP, EAM, CMMS, logistics or engineering systems. It connects the context around the asset.
Systems of record — they record the asset
- ERP
- EAM
- CMMS
- IoT
- Document repositories
- Inspection systems
- Identity
- Knowledge
- Evidence
- History
- Investigation
- Context
- Recommendation
- Human authority
Qualified people — they decide
- Field technician
- Maintenance lead
- Investigator
- Command
Four kinds of collaboration.
These describe intended collaboration models — not existing partnerships.
Defence OEMs
Manufacturers whose equipment is documented, maintained and modified across its life by many hands.
- Authorised technical knowledge attached to each delivered asset and configuration
- Structured feedback from field evidence and recurring findings
- A controlled way for approved documentation to reach the point of use
System integrators & shipyards
Organisations that assemble composite platforms from many sub-systems and support them through refit and upgrade.
- Composite-asset structure that shows the whole, not just the parts
- Configuration and modification history preserved through integration
- An intelligence layer beside existing programme and logistics systems
Technology & knowledge partners
Providers of local AI runtimes, secure infrastructure, document technologies or domain knowledge.
- Local inference and controlled-environment components
- Knowledge-ingestion and document-understanding capability
- Assurance and evaluation tooling
Research & programme collaborators
Institutions and programmes evaluating how evidence-grounded AI can be used responsibly around critical equipment.
- Joint evaluation of grounding, provenance and human-authority controls
- Representative equipment categories for structured evaluation
- Shared learning on responsible AI in controlled environments
Narrow, evidence-led and reversible.
- DiscussAssets, environment and constraints
- ScopeOne equipment category or one unit
- ConfigureKnowledge sources, roles and policy
- EvaluateAgainst criteria agreed up front
- ReviewWhat was established, and what was not
- DecideWhether and how to proceed