About PredictLeads Technographics Dataset
PredictLeads Technographics Dataset delivers structured data on the technologies companies use, collected from sources such as website script tags, DNS records, cookies, and job descriptions. Each technology detection includes first/last seen timestamps and the evidence signals that triggered the detection, and the dataset is accessible via API, flat files, webhooks, and an MCP server for AI agents.
Review
This review covers data quality, access options, common use cases, and practical strengths and limitations of the PredictLeads Technographics Dataset. I assess how the dataset performs for enrichment, lead generation, market monitoring, and integration into automation or agent workflows.
Key Features
- Multi-signal detections: Identifies technologies using script tags, DNS, IP ranges, cookies, and job postings to reduce single-signal mistakes.
- Time-stamped evidence: Every detection includes first_seen and last_seen timestamps so users can analyze adoption trends and migrations over time.
- Multiple delivery methods: Access via a REST API, downloadable flat files, webhooks for real-time updates, and an MCP server for AI agents.
- Enriched metadata: Detections are accompanied by categories, URLs, descriptions, and pricing metadata to support actionable workflows.
- QA and feedback loops: Programmatic anomaly checks and customer feedback channels are used to reduce false positives and improve accuracy.
Pricing and Value
Pricing details are not published on the product page; access is typically provided through tiered or custom plans that reflect API usage, flat file volume, or enterprise support requirements. A sample dataset or trial access is available on request, which helps teams evaluate fit before committing. For teams that need historical detection timestamps and signal-level evidence, the dataset can be a cost-effective source for enrichment, competitive monitoring, and targeted outreach compared with building equivalent detection pipelines in-house.
Pros
- Comprehensive signal set reduces reliance on a single detection method and improves confidence in results.
- First/last seen timestamps and signal evidence enable temporal analyses like adoption curves and migrations.
- Flexible integration options (API, files, webhooks, MCP server) suit a range of technical stacks and automation needs.
- Enrichment metadata makes the data more actionable for sales, GTM, and research workflows.
- Active QA and customer feedback processes help keep false positives in check.
Cons
- Public pricing is not listed, so procurement may require direct sales conversations and custom quoting.
- Coverage and signal quality can vary by company size, region, and privacy settings, which may leave gaps for certain targets.
- Although mitigations exist, no technographics dataset can entirely eliminate stale or false detections; users should plan validation steps for critical workflows.
Overall, PredictLeads Technographics Dataset is best suited for sales and GTM teams, data and ops teams that need enrichment and historical signal context, and investment or research teams tracking technology adoption. It works well when integrated into automation or analytics pipelines where timestamped evidence and multiple access methods provide tangible benefits.
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