AI Competitive Positioning for B2B. Research and stories on how AI engines describe, compare, and recommend companies.https://knitknot.ai/en-ushttps://knitknot.ai/blog/ai-recommends-only-4-7-vendors/https://knitknot.ai/blog/ai-recommends-only-4-7-vendors/Google returns 10 results. AI usually returns 4-7 names. There's no page two. If you're not in the set, you're not in the evaluation.Fri, 14 Aug 2026 00:00:00 GMTAI presencebuyersMax Wiesnerhttps://knitknot.ai/blog/most-of-your-citations-do-nothing/https://knitknot.ai/blog/most-of-your-citations-do-nothing/The average AI answer cites 11.6 sources. Across 76,301 measured claims, fewer than 3 of them actually carry anything the buyer reads. We score every owned page on what its citations earn and trace every false or damaging claim back to the page that supplied it.Mon, 03 Aug 2026 00:00:00 GMTsourcesmethodologyresearchMax Wiesnerhttps://knitknot.ai/blog/ai-cites-pages-that-dont-exist/https://knitknot.ai/blog/ai-cites-pages-that-dont-exist/We probed every URL that ChatGPT, Claude, Perplexity, and Gemini cited across nearly 10,000 buyer-question answers. One citation in eighteen points at a page that is permanently gone, and nearly a third of answers lean on at least one.Sat, 25 Jul 2026 00:00:00 GMTsourcesresearchMax Wiesnerhttps://knitknot.ai/blog/hidden-cost-of-ai-misinformation/https://knitknot.ai/blog/hidden-cost-of-ai-misinformation/When AI gets a fact wrong about your company, it doesn't show up in your CRM as a lost deal. It shows up as a deal that never existed. We tried to quantify what that costs.Wed, 15 Jul 2026 00:00:00 GMTAI presenceresearchMax Wiesnerhttps://knitknot.ai/blog/72-percent-brands-have-factual-errors/https://knitknot.ai/blog/72-percent-brands-have-factual-errors/We analyzed 33,000 AI evaluations across ChatGPT, Claude, Perplexity, and Gemini for 47 B2B companies. 72% had at least one verifiably wrong factual claim. The errors cluster into five predictable, fixable patterns.Sun, 28 Jun 2026 00:00:00 GMTresearchAI presenceMax Wiesnerhttps://knitknot.ai/blog/ten-questions-ai-buyers-ask/https://knitknot.ai/blog/ten-questions-ai-buyers-ask/We generate benchmark prompts grounded in real Google search data, with search volume attached to each one. The questions buyers ask ChatGPT, Claude, Perplexity, and Gemini are more adversarial, more specific, and more comparative than anything your website was designed to handle. Here are the ten patterns that show up most.Thu, 18 Jun 2026 00:00:00 GMTAI presencebuyersMax Wiesnerhttps://knitknot.ai/blog/why-ai-recommends-your-competitor/https://knitknot.ai/blog/why-ai-recommends-your-competitor/We analyzed 33,000 AI evaluations across four models. The most surprising finding: models disagree with each other on who to recommend 48.6% of the time. Which model the buyer opens matters more than most companies realize.Fri, 12 Jun 2026 00:00:00 GMTresearchAI presenceMax Wiesnerhttps://knitknot.ai/blog/what-chatgpt-says-when-buyers-compare/https://knitknot.ai/blog/what-chatgpt-says-when-buyers-compare/We ran the same comparison prompt across ChatGPT, Claude, Perplexity, and Gemini for a B2B company. Four models gave four different answers. Two got the pricing wrong. One recommended the competitor based entirely on the competitor's own blog post.Wed, 10 Jun 2026 00:00:00 GMTbenchmarksAI presenceMax Wiesnerhttps://knitknot.ai/blog/citations-are-ownership-claims/https://knitknot.ai/blog/citations-are-ownership-claims/A source that shaped the AI's recommendation carries more weight than one that provided a background fact. We model which sources had the most influence over what the buyer heard.Sat, 06 Jun 2026 00:00:00 GMTsourcesmethodologyMax Wiesnerhttps://knitknot.ai/blog/ai-is-lying-about-your-company/https://knitknot.ai/blog/ai-is-lying-about-your-company/We pulled every factual claim from our first 2,000 benchmark evaluations and checked them against reality. The error rate was higher than we expected, and the errors weren't random.Wed, 03 Jun 2026 00:00:00 GMTAI presenceresearchMax Wiesnerhttps://knitknot.ai/blog/rebuilding-prompt-generation/https://knitknot.ai/blog/rebuilding-prompt-generation/We designed adversarial prompts to show companies where AI was misrepresenting them. Customers kept getting defensive about the prompts themselves. So we rebuilt the whole thing around real buyer behavior.Thu, 28 May 2026 00:00:00 GMTmethodologyproductKevin Khohttps://knitknot.ai/blog/prompt-libraries-are-coverage-optimization-problems/https://knitknot.ai/blog/prompt-libraries-are-coverage-optimization-problems/A bigger prompt library doesn't mean a better benchmark. We had hundreds of prompts and still missed the buyer situations that mattered most.Thu, 21 May 2026 00:00:00 GMTbenchmarksmethodologyMax Wiesnerhttps://knitknot.ai/blog/approximating-the-claude-engine/https://knitknot.ai/blog/approximating-the-claude-engine/ChatGPT, Perplexity, and Gemini all have incognito search. Claude doesn't. To benchmark how Claude represents companies, we had to find a way that respects Anthropic's terms instead of working around them. Here's what we built.Thu, 14 May 2026 00:00:00 GMTmethodologyengineeringKevin Khohttps://knitknot.ai/blog/confident-lies-are-worse-than-hedged-ones/https://knitknot.ai/blog/confident-lies-are-worse-than-hedged-ones/Accuracy and conviction are independent axes. Most AI benchmarks only measure the first one. We model the interaction between what AI knows and how sure it sounds.Thu, 07 May 2026 00:00:00 GMTmeasurementmethodologyMax Wiesnerhttps://knitknot.ai/blog/brand-health-from-a-recruiting-question/https://knitknot.ai/blog/brand-health-from-a-recruiting-question/A senior leader at a mid-size company asked us to track how AI describes them to candidates weighing offers. It wasn't our use case. With barely any changes, it worked.Wed, 29 Apr 2026 00:00:00 GMTproductbrandKevin Khohttps://knitknot.ai/blog/we-stopped-asking-ai-who-wins/https://knitknot.ai/blog/we-stopped-asking-ai-who-wins/Most LLM-as-judge systems ask one question: who's better? We decompose into structured signals and derive the outcome deterministically. Here's why.Wed, 22 Apr 2026 00:00:00 GMTscoringmethodologyMax Wiesnerhttps://knitknot.ai/blog/introducing-knitknot/https://knitknot.ai/blog/introducing-knitknot/KnitKnot runs the questions software buyers ask across four AI engines, saves every answer, checks the claims, and turns repeated problems into work a company can ship.Tue, 14 Apr 2026 00:00:00 GMTproductbenchmarksKevin Khohttps://knitknot.ai/blog/why-we-pivoted/https://knitknot.ai/blog/why-we-pivoted/We started KnitKnot as a digital sales room. Buyers liked it, but nobody needed it. Then we began asking what a sales room should look like when the buyer is an AI agent.Tue, 07 Apr 2026 00:00:00 GMTlaunchproductKevin Kho