How VerisAI works
Diagnostic reveals symptoms. Audit proves root-cause signals.
You get an evidence-based snapshot of what AI systems believe about your company, and why.
AI Identity
AI Identity is the profile a model infers about your company: what you do, who you serve, where you operate, and how credible you appear. It is derived from the signals the model can resolve—not from intent.
AI Identity Governance
Governance means making identity information machine-verifiable and stable over time: consistent entity anchors, crawlable surfaces, correct canonicalization, and structured data that supports resolution.
Diagnostic prompts
Prompts expose the model’s current interpretation and confidence: categories, claimed services, uncertainty, and contradictions. This is symptom-level output—useful as a baseline.
Baseline first. Root-cause second.
Audit (root-cause signals)
When AI outputs drift from reality, the root cause is usually the observable signal environment: missing identity anchors, inconsistent canonicalization, blocked crawling, thin or contradictory content, or broken structured data. The audit maps claims to signals.
Crawl & indexability
robots.txt, sitemaps, indexability controls, canonical paths, and fetch consistency across variants.
Identity anchors
About/Contact/legal entity signals, locations, ownership, and other machine-resolvable anchors across key pages.
Structured data integrity
Organization / WebSite schema, contact points, identifiers, and validation of critical fields used for entity resolution.
Content clarity
Service taxonomy, positioning language, contradictions, thin pages, and missing context that forces inference.
Deliverables
Outputs are snapshot-based and time-stamped to support repeatable comparison over time.
- AI Identity baseline (what AI claims + confidence patterns)
- Evidence map (claim → supporting signals)
- Forensic crawler/SEO compatibility findings
- Identity drift findings (where interpretation diverges from reality)