The DoneHat blog
Practical reading for teams turning AI answer gaps into useful, measurable work.
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Read articleOur dated self-audits separate branded recall from discovery. Review exact answer-check counts, calibration warnings and a practical measurement workflow.
Read articleA dated source-identification study shows why a working link can still credit the wrong publisher. Use a provenance ledger to choose a practical correction.
Read articleExplore observed Google click rates, then build a search review that separates exposure, visits and useful customer actions. The 2025 study is evidence, not a forecast for your site.
Read articleUse real BBC–EBU research to separate answer quality metrics, preserve the right denominator, and plan a fair recheck. Includes a single-rate uncertainty calculator.
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A long guide for leaders who want AI visibility to become a durable business process instead of a one-off audit.
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A practical guide to improving how ChatGPT-style answers describe your brand without relying on hacks or unsupported ranking claims.
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A practical alternatives guide that separates AI visibility tools, SEO suites, analytics platforms, and broad enterprise AI vendors.
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A plain-language guide to how classic SEO, generative engine optimization, and answer engine optimization fit together.
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A technical buyer guide for teams that need AI visibility evidence inside dashboards, workflows, and reporting systems.
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A detailed operating model for building pages, facts, structured data, and third-party proof that AI answer systems can use.
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A procurement-focused guide for buyers comparing AI visibility tools, price posture, plan limits, and evidence quality.
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