Extruct helps you search for companies using complex natural language queries. It enriches company data, including hard to parse unstructured information, and finds and enriches people’s contacts through a waterfall process across more than 20 sources
SpreadSimple
@refat_ametov hey Refat, though we rather think on co-existing with Clay than competing heads-to-heads here
No Cap
Much needed idea - any benchmarking results you guys can share?
@ednevsky hey Alex,
Sure! Here's a comparison with Exa, Clay and Perplexity we did a while back: https://www.extruct.ai/blog/extruct-data-accuracy/
We've spent 100B+ OpenAI tokens running AI agents to find the design that doesn't hallucinate, perseveres to find difficult data points, and disambiguate entities properly.
@ednevsky thanks! Yeah, it's still WIP on how to define the accuracy for GTM agentic words, but here's our first draft.
https://www.extruct.ai/blog/extruct-data-accuracy/
Extrovert
Congrats, Dany! Sales tech is booming, and you are on the curl's rip! :)
@dmitry_bergelson hey, thanks!
Refero
Interesting concept! I love the idea of skipping filters and just describing what you’re looking for. How accurate are the results in practice? For example, can it handle something like “startups in Europe working on AI agents”?
@mishkadoing the more nuanced queries, the better results.
Here's what we have on this seacrh: https://app.extruct.ai/tables/shared/7f6c63bb-c7cf-4c48-b31d-02f99f072cf3
Remy AI
The idea looks very relevant to me. I'll definitely give it a try! Good luck, guys!
@artyom_zhuravlev thanks! Curious what your case today?
The waterfall process across 20+ sources sounds incredibly robust for contacts.
@rajpurohit_vijesh thanks! so true, and cheper than in Clay