PredictLeads Technographics Dataset provides structured data on what technologies companies use, sourced from company websites, job descriptions, DNS records, cookies, and more. Each detection includes first/last seen timestamps and the signals used, so you can track adoption curves, technology migrations, and competitive shifts over time. Available via API, flat files, and webhooks, with an MCP server for AI agents.
Hey Product Hunt — Roq, Co-Founder of PredictLeads here.
We built the PredictLeads Technographics Dataset to make technographics usable as structured data you can trust. Most datasets don’t show when a technology was last seen or exactly how it was detected.
What you get:
- Technology detections sourced from script tags, DNS records, IP ranges, cookies, and job descriptions
- Each detection includes first_seen / last_seen timestamps and the signals used
- Available via API, flat files, and webhooks
- Includes an MCP server so AI agents can query technographics directly
Common use cases:
- Monitor adoption curves over time to spot growing or declining tools
- Compare competing technologies in the same category to understand market shifts
- Track technology migrations (when companies replace one tool with another)
- Build a Fortune 500 watchlist to see what enterprise teams are adopting
Happy to share a sample or help with queries — ask anything.
A lot of teams get burned by false positives and stale installs; what are the main failure modes you’ve seen in technographics, and what concrete mechanisms did you build to reduce them (recrawl cadence, decay rules, customer feedback loops, suppression lists, etc.)?
Hi @rxever87@lukaiv_pl ! Thats a very interesting idea, I don’t think I’ve come across anything quite like it. I’d love to hear how it originated. Was it driven by an internal need? And who are you mainly building this for?
