Operating Model¶
Delivery system
A repeatable way to turn AI demos into decision-grade systems.
We work backwards from the failure mode that matters most: in high-stakes domains, a confident fabrication is not a minor bug — it’s an unacceptable risk. The operating model below is designed to reduce that risk quickly and measurably.
The engagement loop
1) Clarify the decision
Define the outcome, the unacceptable error modes, and the constraints that must never be violated.
2) Map the domain
Identify entities, processes, mechanisms, and provenance — the minimum semantic skeleton the system must “know”.
3) Encode governance
Turn policy into enforceable rules: constraints, allowed actions, escalation paths, and audit requirements.
4) Build the memory layer
Implement graph memory, connect sources, and produce reasoning traces with stable identifiers and provenance links.
5) Prove it works
Counterfactual tests, red teaming, and monitoring. If it can’t abstain reliably, it’s not ready.
6) Operationalize
Runbooks, ownership, change management, and governance coverage tracking as the domain evolves.
What we optimize for
- Traceability over fluency
- Abstention over improvisation
- Constraints over prompt discipline
- Durable semantics over model loyalty
Models change. Your logic and governance must not.
Typical artifacts (deliverables)
Decision brief
Outcome, unacceptable errors, constraints, and measurement plan.
Domain model
Core entities/processes and their causal relations with source provenance.
Governance package
Constraints, escalation rules, and an audit trail design.
Reasoning traces
Explainable paths (A → B → C) that can be inspected and challenged.
Evaluation suite
Counterfactual tests and red-team cases that validate abstention and compliance behavior.
Runbook
Operational procedures: monitoring, change control, and governance coverage tracking.