Vera · Builds truth
Collects evidence, resolves conflicts, verifies identity, and publishes explainable research conclusions.
VERA CUSTOMER TRUTH & EVIDENCE
Turn fragmented data into Vera’s customer truth and evidence layer
Every customer fact carries source, freshness, confidence, and conflict history—making Vera’s research verifiable and giving Clara and Victor trusted context.
What the customer truth and evidence layer provides
Source-backed customer facts
Every company, contact, and business signal retains its original source, retrieval time, and citation.
Entity and identity resolution
Resolve company aliases, domains, contacts, and cross-source records into one stable customer identity.
Conflict and freshness handling
Compare inconsistent fields, select facts by source trust and recency, and retain change history.
Confidence and evidence strength
Score critical claims and distinguish verified facts, inferred signals, and information still requiring review.
Fit and exclusion decisions
Combine product, ICP, market, and exclusion knowledge into explainable fit scores and rejection reasons.
Trusted cross-worker handoff
Vera passes facts and evidence to Clara and Victor—not just an unsupported summary.
VERA EVIDENCE WORKSPACE
Every Vera conclusion traces back to facts and sources
From multi-source data to a unified customer record, fit decision, and downstream handoff, the evidence chain remains intact so teams can inspect why Vera reached a conclusion.
Vera · Customer truth record
Research task / North American industrial sensor buyers
CONNECTED EVIDENCE SOURCES
Company website and product pages
12 pages · 2 hours ago
Company registry and public filings
3 records · 1 day ago
Company and contact databases
18 fields · 3 days ago
News, hiring, and market signals
7 signals · 6 hours ago
Source policy active
Prefer official and recent sources; inferred claims require support from at least two independent pieces of evidence.
UNIFIED CUSTOMER TRUTH
Northstar Automation
northstar-automation.com · United States · Manufacturing
Company size98%
About 420 employees
Team page + company database
Core business94%
Industrial automation & predictive maintenance
Product pages + solution documents
Target role91%
Director of Reliability Engineering
Org structure + hiring data
Need signal89%
New production-line expansion
Press release + 3 open roles
Exclusion check96%
No exclusion matched
Customer list + competitor and territory rules
VERA RESEARCH DECISION
91
HIGH FIT
Customer fit score / 100
- Product need strongly maps to predictive-maintenance capability
- Company size, industry, and region match the ICP
- Recent expansion and hiring create a credible timing signal
- Contact method and decision role are cross-verified
HANDOFF TO CLARA
Verified decision-maker, four core evidence points, recommended angle, and claims that must not be used.
18 facts · 27 evidence citations · 2 field conflicts resolved
Research replayable · Field changes auditable
How Vera builds verifiable customer truth from sources
Collect source evidence
Read raw material from websites, CRM, databases, public filings, and market signals.
Resolve entity identity
Unify companies, domains, contacts, and aliases while removing duplicates and bad attribution.
Compare conflicting facts
Handle inconsistency using source trust, freshness, and cross-verification.
Score confidence and fit
Separate fact from inference and combine ICP and exclusions into an explainable decision.
Publish and keep current
Deliver evidence-backed truth downstream and refresh it as new signals arrive.