SVC / 02
Use this service when retrieval, vision, or decision support could reduce cognitive load but privacy, provenance, access, and human authority cannot be delegated to a generic AI layer.
Design assistive systems that operate inside approved boundaries and return consequential decisions to qualified humans.
Scope
What the engagement can hold.
- use-case and risk classification
- data and access boundaries
- retrieval or vision architecture
- evaluation corpus and acceptance tests
- human review and override
- monitoring and incident response
Deliverables
What another owner receives.
- approved-use register
- threat and failure model
- reference architecture
- evaluation plan
- human-decision workflow
- operating and monitoring playbook
Signal handoff
How the work moves.
Applied-AI principal paired with the relevant clinical, legal, finance, or industrial decision owner.
- 01classify the decision
- 02bound the data
- 03build the smallest evaluable system
- 04test failure and abstention
- 05instrument human custody
- 06transfer operations
Decision rights
Authority stays named.
- domain owners define permitted use
- security and privacy owners define data access
- principals approve architecture and acceptance
- qualified humans own consequential decisions
Disciplines
The bench follows the work.
- retrieval
- computer vision
- data engineering
- security
- product workflow
- domain review
Quality
Evaluation includes normal cases, boundary cases, adversarial prompts, access-denial tests, abstention behavior, provenance, and human override—not only average-answer quality.
Security
Private or in-environment deployment is preferred where privileged, clinical, financial, or industrial data requires it. Access is inherited from authoritative systems where practical.
Human-directed AI
AI assists. Deterministic controls and named humans decide. The system must reveal sources, uncertainty, and the reason it declined to answer.
Risks we make explicit
What can distort the engagement.
- cross-boundary retrieval
- unsupported confidence
- evaluation drift
- automation bias
- silent provider dependency
Questions before the close
Common objections.
Do you require a public model or external data transfer?
No. The architecture follows the approved environment and data boundary.
Can the system make final decisions?
Not where a clinical, legal, financial, safety, or other consequential judgment belongs to a qualified human.
How do you know it is ready?
Readiness is defined through a versioned evaluation corpus, failure thresholds, access tests, human-override behavior, and an operating owner.
Start with the decision
Bring the priority. We will help bound the work.
Bring the decision, the current operating constraint, and the evidence your receiving owner will need.
Start a conversation.