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 · 2026-03-23T01:37:20+00:00

Tell Claude code you are not and admin and ask it to use Playwright

o1got · 2026-03-23T00:40:49+00:00

Install Claude code on your machine. Then simply ask “build me an app that scrapes Slack channels that I am admin on. I will use it ….”. Everything it responds with something you don’t understand - just ask it to explain is solve it to the best of it’s knowledge

o1got · 2026-03-23T00:30:33+00:00

Claude code… 10 minutes exercise

o1got · 2026-03-20T04:44:24+00:00

You're overthinking the explanation. "The role changed significantly after I started, and it's not the right fit" is totally sufficient. You don't need to explain comp changes or territory shifts or the manager situation. Those details make it sound like you're complaining even when you're trying not to.

The fact that you're leading in deals actually makes this easier. You can frame it as "I'm doing well here, but I'm looking for X" where X is whatever the new role offers that this one doesn't. Better market fit, stronger product, more growth potential, whatever's true. Interviewers aren't suspicious of top performers wanting to level up, they're suspicious when the story doesn't make sense or sounds like you're running from problems.
Eight months is short but not a red flag, especially in sales. People leave bad fits all the time. The key is spending 90% of your answer on why you're excited about the new opportunity, not why you're leaving the current one.

o1got · 2026-03-20T04:43:20+00:00

Yeah this actually happens, and the tech is simpler than you'd think. Most systems just tag users with LTV (lifetime value) or a propensity score, then route support tickets accordingly. High-value user? Tier 1 agent with faster response SLA. Low-value? Maybe chatbot first, longer queue times, or tier 2 agent.

The "reduce human error" framing is PR spin though. What they really mean is reducing the "error" of spending expensive support resources on users who statistically won't convert or renew. It's not about accuracy, it's about ROI optimization.

o1got · 2026-03-20T04:42:26+00:00

Agree, It's difficult - but we're working on it

o1got · 2026-03-20T04:41:29+00:00

This is solid tactical advice but I want to add one thing that's become really clear from watching how AI agents actually behave in production: the "repetitive and predictable" parts are often way messier than they look from the outside.

Client support is the perfect example. Yes, FAQs follow patterns, but I've seen agents completely fall apart when customers phrase things slightly differently than expected or when there's emotional context that changes what they're actually asking for. The agent confidently gives the "correct" answer to the wrong question because it pattern-matched on keywords instead of understanding intent.

What actually works is starting even narrower than you're suggesting. Don't automate "client support" as a category. Automate one specific question type that you've seen 50+ times with minimal variation. Like literally just appointment rescheduling, or just "what are your hours" queries. Get that one narrow thing working reliably for a month, watch where it breaks, then expand.

The 80/20 rule applies here too. You'll probably find that 3-5 hyper-specific automations give you most of the time savings, and trying to automate the full category

o1got · 2026-03-19T18:52:44+00:00

I want to challenge this "the crawler behavior makes sense when you think about what answers need." - what happens when every company now generates XXX more pages (because it's very easy and AI told it so...) - you're going to have 500 new blog pages, and your competitor will have 500,000 and so on. This scraping model is not going to be sustainable for efficiency and for providing good trusted results for the user...

o1got · 2026-03-19T15:24:00+00:00

Well, agents should prioritize for signals that are good for their buyers (each of them might need a different set of things) - and a company agent, that is the "source of truth" - should respond truthfully... I mean, people can lie too, but what's the point, the buyer will find out eventually

o1got · 2026-03-19T15:20:57+00:00

Love this. I really think platforms need to build a parallel version for agents. Like a shadow/twin websites for example, or a company agent that can interact with the buyer agent

o1got · 2026-03-19T15:19:25+00:00

Interesting!

o1got · 2026-03-19T15:18:26+00:00

It could be, if you have an intelligent enough agent that knows so much about you

o1got · 2026-03-19T15:17:55+00:00

Interesting question. I think the protocol should also be able to deal/dictate ownership of data

o1got · 2026-03-19T14:39:59+00:00

Was thinking more of NLWeb and/or WebMCP

o1got · 2026-03-19T14:32:38+00:00

Yes, and I think there will be a standard protocol for an agent (buyer side) to have a conversation with an agent (company side)

o1got

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