Trust starts with evidence your team can inspect.
VexASI designs AI workflows around visible sources, explicit review gates, data-use boundaries, and service limits. The goal is useful AI-assisted work without hiding how the record was produced.
The trust model is simple: source first, review before trust, boundaries before scale.
VexASI does not ask buyers to trust generic AI output. The workflow keeps evidence, confidence, reviewer state, and handoff fields visible so each record can be inspected before action.
Source traceability
Records should retain the quote, URL, file reference, page, observed date, entity match, or source locator needed to inspect the claim.
Human gate
AI assistance does not remove review ownership. Qualified outputs carry review status before they are treated as usable work.
Clear service boundaries
Signaling is not drawing review. Peer Review Services are not market signal reporting, AHJ representation, code enforcement, permit approval, stamping, or responsible-control services.
Trust Resources
Use these pages to understand how VexASI handles data, evidence, confidence, and service boundaries.
Data Use
How VexASI separates public evidence, inquiry data, private source material, and training candidates.
Security Boundaries
How the public website, private source material, internal tooling, and AI-assisted review work stay separated.
Signal Confidence Rubric
How source strength, specificity, recency, false-positive risk, and review status shape confidence.
Sample Signal Brief
A synthetic example showing how a VexASI Signaling record can preserve evidence, confidence, and action fields.
Use the narrowest workflow that can prove value.
Start with one evidence-heavy process, a defined source set, and a review owner before expanding automation.