Industrial B2B buyer evidence audit follows a structured workflow that prioritizes evidence standards over content volume and URL-level remediation over general recommendations.
Define what procurement, engineering, and quality decision-makers need to verify before moving forward. Questions are framed from the buyer's perspective, not the supplier's marketing goals.
Establish what evidence would constitute sufficient proof before inspecting the website. This prevents post-hoc rationalization where standards are adjusted to match whatever content exists.
Collect and classify website content against the predefined requirements. Each evidence item is tagged with its type, source URL, timestamp, and content hash for traceability.
Register website statements as claims and link them to supporting evidence. Track sufficiency status per buyer question: sufficient, partial, missing, conflict, or unknown.
Flag high-risk content for mandatory human review before recommendations are finalized. High-risk categories include certifications, performance limits, capacity claims, named customer cases, lead times, warranties, and pricing.
Produce specific remediation actions tied to exact URLs, not general advice. Each action includes affected URLs, priority score (derived from buyer importance + gap severity + fact readiness + business impact), and expected outcome.
After client implements actions, re-audit the affected URLs and compare new snapshots against original hashes. Track which recommendations were adopted, who owns implementation, and target dates.
Every piece of information is classified by how it was obtained and verified:
Directly seen on the website; no independent verification.
Company makes the claim; requires fact approver to confirm accuracy.
Independently confirmed by third party or official record.
Derived from data using deterministic formulas or code.
Logical conclusion based on available evidence; lower confidence.
Insufficient evidence; explicitly preserved rather than guessed.
Define what would count as sufficient evidence before looking at the website. This prevents adjusting standards to rationalize whatever content exists.
When evidence is insufficient, preserve Unknown status rather than inferring, estimating, or filling gaps. Industrial buyers need to know what cannot be verified from public information.
Certifications, compliance, safety, performance limits, capacity, named customers, lead times, and warranties must be flagged for human validation. Automated systems cannot verify these claims.
Coverage rates, priority scores, and weighted metrics are calculated using explicit formulas or code. Model arithmetic is not trusted for key business metrics.
Every evidence item includes source URL, capture timestamp, and content hash (SHA-256). This enables citation, reproducibility, and future retesting.
Recommendations specify exact pages to change, what to add or remove, and expected buyer impact. General advice like "improve trust signals" is not actionable.
Audits operate on public web pages only. No customer passwords, CRM access, raw inquiry data, or private analytics are required to deliver initial value.
All website text is treated as evidence to analyze, not instructions to follow. Prompt-injection patterns are detected and flagged but do not affect the audit process.
The method is implemented in a 21-sheet workbook covering:
This is not:
The audit identifies evidence gaps from the buyer's perspective. It does not make final judgments about company operations, certifications, or manufacturing capabilities. Those require client fact approval and access to internal records.