How independent verification works
Four steps take a target from observation to a conclusion that can be checked. You choose the target; V1LA does the discovery.
Observe, Detect, Contrast, Verify
- 01
Observe
V1LA studies the target from the outside, with no access required.
- Public documents, websites and interfaces
- User-facing processes and observable outcomes
- Policies, communications and third-party dependencies
- 02
Detect
It extracts what is claimed and what does not add up.
- Claims that can be tested
- Inconsistencies between sources
- Evidence gaps and open questions
- 03
Contrast
Findings are put to the organization and to additional evidence.
- A contrast request to the organization
- Its response is recorded as evidence
- Authorization is requested only if internal verification is needed
- 04
Verify
Each claim receives a status and a traceable justification.
- Hashed, timestamped evidence
- A status for every claim
- Human review before anything is reported
Start outside. Go deeper with authorization.
The path to the internal layer is a decision of the organization, taken after the external findings are on the table.
External / Organizational Layer
No access to internal systems required.
- Public claims
- Public policies
- Websites
- Interfaces
- User-facing processes
- Organizational behaviour
- Public documentation
- Observable outcomes
- Third-party dependencies
- Communications
- Inconsistencies
- Evidence gaps
Internal Agentic Layer — with authorization
Scoped to what the organization authorizes.
- AI agents
- Agentic workflows
- Model interactions
- Prompts
- Tool calls
- Retrieval
- Handoffs
- Logs
- Access permissions
- Decision paths
- Human intervention
- Internal policies
- Evidence provenance
- Traceability
- Reproducibility
Authorization is explicit, scoped and revocable.
Where people decide
V1LA does not issue verdicts on its own. A reviewer approves or escalates each finding, and the report shows who reviewed what. If a finding cannot be settled, it stays open and says so.
Evaluations are designed so they can be run again, which is what lets a third party reproduce a conclusion instead of taking it on trust.