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PredictLeads Technographics Dataset

PredictLeads Technographics Dataset

Source-backed technographics with an API and MCP server.

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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.
PredictLeads Technographics Dataset gallery image
PredictLeads Technographics Dataset gallery image
PredictLeads Technographics Dataset gallery image
PredictLeads Technographics Dataset gallery image
PredictLeads Technographics Dataset gallery image
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Launch tags:API•Artificial Intelligence•Data
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What do you think? …

Roq Xever
PredictLeads Technographics Dataset
Maker
📌
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.
Ksenia Sh

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?

Thanks in advance and congrats on the launch!

Roq Xever
PredictLeads Technographics Dataset
Maker

@ksenia_sh Thanks a lot!

It actually wasn’t driven by an internal need. We’re a data provider, and this came directly from what our customers and partners were asking for. We kept seeing a strong demand for reliable technographics, so we built it as a dataset they could plug straight into their workflows.

Our main users today are sales platforms and sales teams using it for targeting, enrichment, and GTM use cases. Interestingly, we also see quite a few tech-focused investment firms using it to track tech adoption and changes across companies.

Rishika Sharma
Supaboard AI

What's the core offering of your product?

Roq Xever
PredictLeads Technographics Dataset
Maker

@rishika_sharma9 Hey Rishika, thanks for the question!

Our core offering is company-level technology detection - essentially understanding what technologies a company is using (e.g. HubSpot, Marketo, etc.).

On top of that, we enrich each technology with detailed metadata like URLs, categories, descriptions, and even pricing, so teams can actually act on the data rather than just see a tech name. Happy to help with any other questions!

Aleksandar Blazhev

Congrats on the launch team! Good luck!

Roq Xever
PredictLeads Technographics Dataset
Maker

@byalexai Thanks Aleksandar, we appreciate it!

Curious Kitty
Product Hunt
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.)?
Roq Xever
PredictLeads Technographics Dataset
Maker

@curiouskitty Great questions, thank you so much!

Every detection comes with clear evidence and an explanation of how it was identified. To keep false positives low, we combine multiple signals rather than relying on just one. Our high-accuracy job openings data (which we already supply to global job boards) is especially helpful for detecting behind-the-firewall technologies.

On top of that, we run programmatic anomaly checks to flag anything suspicious, and we have a dedicated QA team that manually reviews and fixes edge cases.

We also keep a close feedback loop with customers - there’s 24/7 support and direct reporting channels, and anything flagged goes straight back into QA and detection improvements.

Happy to help with any other questions!