Tejas Shukla

Audits HTML documents for accessibility tree integrity, semantic tag hygiene, and agent extractability affordances. Identifies structural bottlenecks that impair autonomous web agents and LLM scrapers. Use when diagnosing why a brand is missing from AI assistants or when evaluating agent actionability

Audit a website for AI-discoverability and on-site-engagement problems — crawlability, JS-render gaps, missing/invalid structured data, facts locked in non-text, stale or uncorroborated facts, entity ambiguity, weak on-site orientation / no context retention — and produce a report of findings plus prioritized, actionable fixes. Use when diagnosing why a brand is missing or misrepresented in AI assistants, or why visitors who arrive don't engage.

Evaluates the visible text of a webpage for AI discoverability quality. Checks factual correctness of major claims, detects contextual drift and undefined jargon that could mislead AI chunkers, and flags low information density paragraphs that rely on marketing fluff over concrete facts.

Audits whether citation visitors can quickly confirm quoted facts by checking bottom-line-first paragraphs and fragment-based deep links. Use when evaluating on-site engagement and context retention after an AI-generated citation.

Evaluates temporal freshness signals, external trust-claim corroboration, and entity disambiguation on a target domain. Detects stale copyright dates, outdated content publication/modification dates, uncorroborated performance claims, unattributed testimonials, and absence of any freshness indicators.

Audits a website for machine-readable semantic context, specifically evaluating the presence and quality of an llms.txt file and schema.org JSON-LD structured data. Use when diagnosing why a brand's specific facts or entity definitions are missed by AI assistants.