Purpose and decision boundary
We define the business job, allowed actions, affected people, failure costs, and the decisions that must remain human-owned before selecting a model.
Trust & governance
Controls that travel with the system
Governance is part of architecture, evaluation, release, and operations—not a policy document added after the pilot. We adapt the control depth to the workflow, data, affected people, and failure cost.
Delivery control model
Six questions every production workflow must answer
Data and access
We map data sources, purpose, retention, residency, provider processing, and role-based access. Sensitive production data is not required for an initial sample-data proof.
Grounding and tool use
Knowledge systems preserve permissions and evidence. Tools are narrow, validated server-side, separated into read and write operations, and audited where practical.
Evaluation and release
Representative tasks, edge cases, and failure modes become a versioned evaluation set. Quality, safety, latency, and cost are reviewed separately before release.
Human oversight
Users can identify automated interactions, confirm consequential actions, challenge an outcome, and reach a responsible person through a documented escalation path.
Operations and change
Models, prompts, retrieval, integrations, and policies change. We establish tracing, incident handling, rollback, review cadence, and an accountable service owner.
Evidence, not slogans
What an engagement can leave behind
- Use-case and risk register
- Data and provider flow map
- Permission and tool-action matrix
- Evaluation plan and release thresholds
- Human handoff and incident runbooks
- Architecture, operating model, and residual-risk record
Important boundary
Technology does not create compliance by itself
Capilano AI provides technical and operational consulting, not legal, regulatory, clinical, financial, or certification advice. Applicable obligations, controller and processor roles, sector rules, and residual risk require review by the client’s qualified stakeholders.
Our delivery model is informed by the voluntary NIST AI Risk Management Framework and its generative AI profile, then tailored to the organization and use case.
Bring one workflow and its hardest failure mode
We can map the decision boundary, evidence, controls, and smallest credible proof before committing to a platform.