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Details cleaned_saas_webscrape attributes, collection methodology, and follow on items.
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Collected as a part of a larger effort to build a recommender system for organizations looking to optimize their SaaS stack. This cleaned subset represents about 25% of the total raw set. The raw set (coming soon) also includes information such as reviews, overall ratings, application category (accounting, project management, VoIP, etc.), and top ten alternatives for each.
The app names and descriptions were collected using selenium and a headless chrome driver and the site URL's were obtained using a private metasearch engine: (https://github.com/searxng/searxng) attached to a local llama 7b: (https://github.com/ollama/ollama).
Full raw set (and maybe the metasearch enabled llm data collection script) coming soon.
A collection of over 12,000 SaaS applications, descriptions, and the product site URL for each.
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Thanks for sharing ! I downloaded dataset you shared, and I can see it being really useful as I do competitor research. $10 to save me 10s of hours feels well worth it to me.
You should share the recommender system here when it's ready. If you can expose it with an API, you could add it to the Water layer as a service. Then organizations can interact with your recommender, inputting their current software stack and getting recommendations for better or cheaper alternatives.
Someone could build an automated solution to easily port data from one SaaS to another so that organizations can easy migrate to your recommendation.
This could save orgs a ton of money!