Mindcase is the infrastructure layer for extracting web data in a structured, usable format. Built for developers and AI teams that need reliable web data without managing scraping infrastructure. Access APIs across popular sources, or get anything across the web built as a custom API for your specific use case.
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Congratulations on the launch!
answering Saurabh's question directly - we're a B2B SaaS and the source I'd actually pay for is G2/Capterra review pages for our competitors: review text, star rating, and reviewer company size band. Maybe 500-1000 rows a month, nothing crazy. Every competitive intel tool I've tried treats review sites as an afterthought behind the "big" platforms. Also curious about the pricing question Raunak asked above - if I fetch the same post twice in a day, am I paying twice or is there any caching on the backend?
@galdayan Appreciate you answering the question properly Gal. Review text, rating and reviewer size band across G2 and Capterra at around 1,000 rows a month is a well shaped request and we will come back to you in this thread with a straight yes or no on both sites rather than a maybe.
On pricing, you pay per collection, so calling the same page twice bills twice. Whether that matters depends entirely on the source. For reddit or twitter, as I said to Raunak above, the second call is usually the point, since the upvotes and comment counts have moved and you are collecting a second observation rather than the same row. Reviews sit at the other end of that. They accumulate rather than change, so a scheduled pull plus a dedupe on review id at your end is cheaper and gives you the same picture.
The custom API angle is interesting. A lot of scraping tools work well for known sources, but the anything across the web problem is where teams usually get stuck.
@sansa_grey Thank you, and worth saying plainly: anything across the web is the ambition, not a guarantee.
Some sources are genuinely a bad idea and we would rather tell you that than take the work and disappoint you two months in. What we can commit to is a straight answer either way, quickly.
If you have a source that has stalled a project before, name it and we will tell you honestly where it sits.
Normalizing web extraction behind a single API call shape is a total game-changer for AI teams. Super cool build, congrats!
@thisiskp_ Thank you KP, so glad you like it.
That was the main thing we wanted to get right. Good to hear that comes across from outside.
This is actually pretty useful. Keeping scrapers working is such a pain, so having it all behind one API makes a lot of sense. Nice launch 👏
@rajat_kapoor05 Putting it behind an API is partly a technical decision and mostly a way of making sure that knowledge is not sitting with one person.
Reliable web data is still one of the biggest bottlenecks for AI applications. Simplifying extraction without teams having to maintain complex scraping setups feels like a valuable infrastructure layer. Great launch!
@better_shab Appreciate that. What surprised us building it is how much of the bottleneck is maintenance rather than the first extraction. Getting data out of a site once is a fun afternoon. Keeping it correct across layout changes, rate limits and anti-bot for a year is the actual job, and that is the part teams underestimate when they decide to keep it in house.