Inspiration
The idea came from personal frustration. While working at Lendable, we spent weeks trying to figure out which tools to buy. Conversations with other software buyers (CTOs, founders, ops managers) revealed it wasn’t just us — everyone hated this process. The existing review sites felt biased toward vendors, not buyers.
What We Learned
The biggest lesson was that buyers don't just want "the best" software — they want the right software for them. To solve this, we had to build AI that can analyze features in the context of each buyer's specific needs, not just spit out generic rankings.
How We Built It
We combined structured (like pricing) and unstructured data (like YouTube reviews) from multiple sources. AI models then analyze this data to suggest software that fits a buyer’s exact criteria. Our platform lets users compare products side-by-side and see where they differ.
Challenges We Faced
Messy Data: Merging and analyzing content from multiple sources without duplications or junk data. Trust: Making sure buyers see the platform as unbiased and useful, especially compared to existing review sites. Cold Start: Building value with limited initial data, solved by pulling in as much public info as possible.
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