Glimpse

@vikramp7470 Actually like that idea a lot & it's exactly in line with the core thesis of the platform: not give you raw signals to process, but actual 'next steps'. At the moment we already 'surface the gaps & opportunities', for both AI search & regular Search Engines, but adding the next step after that would make a lot of sense (and it's not a heavy lift to build, we have most of it in place to do this quickly). Thanks for the tip!

Hey everyone! Sven here, founder of Glimpse. 👋 We built Glimpse because tracking competitors by hand doesn't scale. By the time you spot a pricing change or a new ad campaign, your team is already a step behind. Glimpse watches your competitors automatically - content, ads, pricing, hiring, website and tech-stack changes, reviews, even where they show up in AI answers - 20+ signal types in all. When something moves, you know about it before your next coffee. The part I'm most excited about is the AI agent on top. It doesn't just collect data - it tells you why a change matters and what to do about it, keeps your sales battle cards current as competitors evolve, and flags the demand you're losing in AI search before a deal even exists. It fits the tools you already use too: Slack alerts, a REST API, and native Zapier, Make, and n8n support, so you can trigger whatever automation makes sense. No new dashboard to babysit. We're early and building fast, and there's a free trial if you want to point it at your own competitors. Would love to hear: what competitive-intel problem keeps you up at night?

Genuinely useful tool, congrats 👏 @sven_de_meyere1 If the agent finds that a competitor is getting heavy traction in AI search summaries, qq can it generate content briefs to help us close that specific gap?

@andrasczeizel We have 25 prompts that we track across all major LLMs. We provide a list of suggested prompts based on your own website & the competitor's website, but you can also add your own! The AI visibility score allows you to track your progress over time.

Congrats on the launch! This looks really interesting. One thing I'm curious about: does Glimpse include a competitive analysis feature where you can compare companies across product features, positioning, social media presence, and distribution channels? That would make it an incredibly powerful research tool.

I like the idea of keeping up your competitor's data up to date but my first thought is about accuracy, how do you validate the information before it's surfaced ?

@reda_roqai_chaoui Usually because we get the data straight from the original source: public ad libraries, competitor's website, social channels, etc.. So it actually parses the original raw data first, then we enrich it through our own engine & then convert it into analysis & recommended actions. So it's not a LLM wrapper that hallucinates random insights.

@daniela_pilla (at this stage) we don't compare on a feature-by-feature basis as such, but we do track 'new features' for example. And all of the other things like social presence & distribution channels is exactly what Glimpse does. We monitor both organic & paid channels and give you a full insight in which ads your competitors run, for how long, which USPs they use, etc..

Competitive intel is one of those things everyone knows they should do, but very few teams actually keep updated consistently. ads, pricing, hiring, reviews, positioning, website changes... it all moves too fast to track manually unless someone basically owns it full-time. The AI search part is especially interesting to me. it feels like competitors are not only fighting for attention on Google or social anymore, but also inside the answers buyers get before they even visit a website. Curious how Glimpse measures that lost demand in AI answers. are you tracking specific buyer queries over time, or comparing how often each competitor gets recommended across different models?

@sven_de_meyere1 Spot on Bridging that gap from data to actual action steps is exactly why people love good AI tools, Good luck with the rest of the launch🙌

@sven_de_meyere1 Thanks for the reply, loved the "full insight in which ads our competitors run" Definitely I will tried out and get back

@vikramp7470 Appreciate it!

@daniela_pilla Amazing! Would love to hear your feedback!

@andrasczeizel  @sven_de_meyere1  Interesting angle, especially around measuring demand lost inside AI answers. On the AI search side, are you checking prompts through API results only, or also through the actual web experiences like ChatGPT, Perplexity, Gemini, AI Overviews, etc.? And when you run those checks, can the answers be tested from a specific market/location, or are they measured from one default/global environment?

@andrasczeizel  @viseph Today we cover five surfaces: ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. For the four LLMs we run each prompt as a live model response with web search on, so the answers reflect real-time retrieval rather than static training data. AI Overviews we read from the rendered SERP. To be straight about it: it's not the logged-in consumer apps (no personalization or chat memory), and it's not raw base-model API either. It sits in between, live and web-grounded but through a neutral collection layer, which is deliberate so results stay reproducible and comparable week over week. On location: right now it's one default environment (US / English). Per-market and per-language testing is the next thing we're building here. Curious where you're seeing the biggest market-to-market divergence, since that's exactly what's pushing us on it.

the "not an LLM wrapper, we parse the raw source first" answer to the accuracy question was a good one, that distinction matters more than most people asking about AI tools realize. separate question though - if everyone in a category eventually runs something like Glimpse, competitors watching competitors becomes symmetric, and pricing/ad pages start getting hit by a lot of automated traffic that isn't real customers. do you see any sign of companies changing behavior because they know tools like this are watching, like cloaking pricing pages or running decoy ad variants specifically for competitor-tracking bots versus real users

@galdayan What I actually see is the incentive running the other way right now. Companies are making their pricing and positioning pages MORE bot-legible, not less, because LLM crawlers (ChatGPT, Perplexity, Claude) have become a real buyer discovery channel. If you cloak your pricing page against automated traffic, you're also cloaking it against the answer engines your prospects are asking. That trade is a bad one, so almost nobody makes it. The "cloaking" that does exist is mostly the old-fashioned kind: sales-led companies hiding pricing behind "talk to sales." That predates CI tools by a decade and it's about deal-level price discrimination, not bots. Decoy ad variants: haven't seen it in B2B SaaS. Ad libraries on Meta and LinkedIn are already public by regulation, so the cost of running fake variants (confusing your own performance data, wasting spend) outweighs fooling a competitor's dashboard. On symmetry, I think you're right that it becomes table stakes, same as everyone having analytics. But detection was never really the moat. Everyone can see a competitor shipped a pricing change. Very few teams interpret it correctly and respond within the week instead of finding out from a lost deal three months later. That gap is where the value stays even when watching is symmetric.

Positioning it as a competitive intelligence agent instead of another dashboard makes the workflow easier to picture. Do most teams use it for ongoing monitoring, or is it more common for one-off market research projects?

@amjad_shaik It's meant for ongoing monitoring, exactly to avoid that one-off research to become stale & outdated really quickly.

@sven_de_meyere1 That makes sense. I can see why static reports lose value quickly in fast-moving markets. Thanks for clarifying. I hadn’t considered how quickly competitive data becomes outdated.