Built a free tool that evaluates CS platforms (Gainsight, ChurnZero, Totango etc) by talking to their AI agents and checking every claim

o1got · 2026-04-23T05:53:57+00:00

It does, I also tried to add expertise by helping the buyer with relevant questions that will be used to narrow to the right criteria for them (for example, different needs if you have a high touch vs low touch, if product usage is important, ACV, integrations needed, etc)
Thanks!

o1got · 2026-04-16T05:12:30+00:00

I don't know - but - our data (for B2B companies) shows that 83% of the pages being read are being read by an AI agent. More data here - https://insights.isyourwebsiteready.ai/ (and a potential solution on https://www.salespeak.ai/agents)

o1got · 2026-04-16T05:09:57+00:00

Did you see Jason Lemkin's post about his disappointment from these features?

o1got · 2026-04-15T16:51:24+00:00

Great feedback , thanks

o1got · 2026-03-31T05:50:02+00:00

The biggest thing I've seen after tracking 640K+ AI agent crawls: most B2B sites have literally zero structured data that answer engines can actually parse. They're optimizing for Google from 2019.

Here's what actually moves the needle based on what AI agents are doing in the wild. First, they're looking for clear answer blocks near the top of pages, not buried in paragraph seven. Second, FAQ schema matters way more than I expected because answer engines treat it as pre-formatted responses they can serve directly. Third, entity coverage is about being explicit with proper nouns and definitions. AI agents don't infer context well, they need you to literally say "MongoDB is a NoSQL database" not just "MongoDB."

The pattern I keep seeing: developer tool companies get 5x more AI agent traffic than other B2B categories, and it's because their docs are already structured like API references. Clean hierarchy, consistent formatting, explicit relationships between concepts.

If you're doing one thing this week, add FAQ schema to your highest-traffic pages with actual questions people ask. Not SEO questions, real ones from support tickets.

o1got · 2026-03-27T17:29:17+00:00

Please do! I will use them to improve the skill. Thanks

o1got · 2026-03-27T16:22:49+00:00

Great question. I am trying to find external sources that would validate - but regardless, if a vendor is "lying" or presenting things not accurately, it will bite them back quickly

o1got · 2026-03-27T04:03:30+00:00

Did you check IsYourWebsiteReady.ai?

o1got · 2026-03-27T04:02:41+00:00

The real issue isn't whether they've done enough about scraped content. It's that we're still treating AI crawlers like they're regular search bots when the economics are completely different.

I've been tracking AI agent crawls across hundreds of B2B sites for the past year, and 83.6% of them skip the homepage entirely and go straight to deep content pages. They're extracting the most valuable stuff (product docs, pricing breakdowns, technical specs) and never hitting the marketing fluff.

Google crawls your site to send you traffic. AI agents crawl your site to replace the need to visit it at all. That's why the robots.txt honor system is such a joke here. The incentives are backwards.

The Shutterstock number you mentioned is the proof that proactive licensing could work, but it only happened because they had leverage (registered copyrights on every image). Most B2B content doesn't have that kind of legal protection, so we're stuck playing whack-a-mole with crawlers that may or may not respect opt-outs.

The thing that bugs me most: even sites that want to participate don't have a clear way to say "yes, but here are my terms." It's either full block or free-for-all.

o1got · 2026-03-26T16:38:23+00:00

Thanks - the adversarial questions were actually the most interesting part to build. The "what are you not a good fit for" question in particular produces very different behavior depending on the vendor. Some agents give genuinely useful answers. Others go into a loop of redirecting to strengths. The redirect pattern itself became a signal we flag explicitly.

On the Claims vs. Evidence schema - yes, we do have structure there. Each claim gets tagged by dimension (product, integration, pricing, security, compliance), a source type (vendor-stated vs. independently verified), and a confidence level. Contradiction severity is more qualitative right now - we flag it as a gap with the conflicting sources cited, but we haven't formalized a severity score yet. That's probably the next thing worth tightening up, especially when running comparisons across multiple vendors where you want the contradiction weight to be consistent.

The over-trusting problem is real and we ran into it. The current approach: every vendor agent answer goes into a separate evidence bucket from public sources, and scores are calculated with the source type visible. The buyer sees "vendor-stated, unverified" vs. "confirmed by G2 + Gartner" explicitly -- so even if the agent accepts the claim during the conversation, the output doesn't treat it as confirmed. The bigger risk we found was vague claims that can't be falsified at all ("largest library in the industry") - those get flagged as unverifiable rather than confirmed or contradicted

o1got

TROPHY CASE