AnVendor – see your competitor's customers

Hacker News

Long story short: I found a way to reveal which SaaS any company uses. And estimate how much they pay for it. And now in detail: I'm a solo entrepreneur – an ML Engineer, a bit of a product manager, and a self-taught marketer. At some point, I thought it would be nice to detect what services companies use. There are solutions that scan websites for frameworks and APIs used, or that analyze a company's job postings, and others that look at news and website publications. But overall, no single approach can determine what SaaS services are being used right now. Only in the past or in the future. So I created an engine that does this (not right away, of course, but after almost six months of experimentation). Unfortunately, I can't go into detail about how it works; that's the main secret of my service. It's not hacking, cracking, or secret databases. It's just a little ingenuity. And there's absolutely no AI – just engineering and classic ML. The engine itself can only detect the fact of a "subscription" from any company. "Subscription" is in quotation marks because that's not entirely accurate – it's more likely that the company interacts with SaaS. Sometimes it might be a pilot, sometimes a parent or subsidiary company. Nevertheless, it works. To make this product useful for lead generation, I added a second layer – an estimate of how much the company spends on services. This collects service rates, the number of employees, and a bunch of other parameters, plus a cost estimation model. This is a rough estimate, as there are situations like pilots, special negotiating terms, and discounts for enterprises. However, this estimate will improve as more data is accumulated. Currently, I've added ~700 SaaS services that can be detected. And I really need a signal from real users about which services should be added – the service has a free plan, and all service requests are stored in the database. Overall, I'd appreciate any feedback.

Visit product View on Hacker NewsAug 30, 2026

Share card

Actual performance

1points

1comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Another Tailscale Competitor46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Another Tailscale Competitor

Hacker News1

They chose your competitor. Find out why46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

They chose your competitor. Find out why

Hacker News6

btw

Delight your customers

BetaList

Identifying, Segmenting and Contacting our Customers51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Identifying, Segmenting and Contacting our Customers

Hacker News11

Our first three featured customers57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Our first three featured customers

Hacker News3

New competitor to cratejoy40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New competitor to cratejoy

Hacker News1

Competitor Teardown30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

One-page competitor teardown in 48 hours or refund

Indie Hackerscommitment-side-project

YouTube Competitor?42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

YouTube Competitor?

Hacker News3

We're building a Flightfox competitor on steroids52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We're building a Flightfox competitor on steroids

Hacker News2

Find your competitor's Websites51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find your competitor's Websites

Hacker News31

Browse all products Analyze your launch