Guides
Practical guides on websites, email, security and AI automation, picked for ai companies.
ChatGPT referral traffic dropped around October 9, 2026 because many links lost their utm_source tag. How to check GA4, annotate the dip and measure fairly.
How to get cited by Claude: allow Claude-SearchBot, write quotable answer-first pages, keep facts current, and track Claude referral traffic in GA4.
Which domain extension is best for your business? Compare .com, .ai, .io, .co, country codes and new gTLDs on SEO, trust, cost, eligibility and risk.
An LLM evaluation workflow for AI product teams: versioned test sets, evals on every change, regression thresholds, human review and a clear release gate.
An AI startup website checklist for UK founders: AI crawler controls, llms.txt, trust and security pages, headers, and claims that satisfy the ASA and CMA.
A practical Open Graph tags guide: set og:title, og:image and twitter:card so your links look right on Facebook, LinkedIn, WhatsApp, Slack and X.
How ChatGPT search chooses sources to cite: OAI-SearchBot, search partners, query rewriting and freshness, plus what you can control and how to track referrals.
Should you block AI crawlers like GPTBot, ClaudeBot and PerplexityBot? Learn which bots train models, which power AI search, and what each block costs you.
llms.txt is a proposed Markdown file that summarises your site for AI tools. Google Search ignores it, so most businesses do not need one. See when it helps.
Warm up a new email domain or IP by authenticating first, starting with engaged contacts and raising volume gradually. A practical schedule and checklist.
To get mentioned in ChatGPT and AI answers, let AI search crawlers in, publish clear, citable facts and build a real reputation. Nobody can guarantee it.
Free checklist (PDF)
31 practical checks for ai companies: website speed and mobile, Google Business Profile and reviews, email security, booking and enquiry handling, and AI search visibility. Each one says why it matters, and many link to a free tool that checks it.
AI teams are asked to move quickly and to explain themselves clearly, often in the same week. The work around the model, from evals to changelogs to safety reviews, is what turns a clever system into one people can rely on.
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