A cybersecurity ratings provider sourcing internet-scale data for their own product.

A leading US-headquartered cybersecurity ratings provider builds external risk scoring across millions of organizations. The product depends on continuous internet-scale visibility; sourcing that visibility from Deepinfo's dataset (rather than running internal collection) lets the engineering team focus on scoring and product, not on running an internet observatory.

THE CUSTOMER

US-headquartered cybersecurity ratings provider with millions of organizations rated.

A US-based cybersecurity ratings and external risk scoring vendor serving enterprise customers worldwide. The product catalogues and scores millions of organizations on continuous external posture; customers use the ratings for vendor risk management, M&A due diligence, and self-assessment benchmarking.

Building the product at scale requires internet-wide visibility into domain registrations, certificates, DNS state, port exposure, web technologies, and CVEs across the full surface. Building the data-collection infrastructure to support that internally would be a substantial separate engineering investment; sourcing the data layer from Deepinfo's API and feeds let the team scale faster.

THE CHALLENGE

Sourcing the data layer instead of building it.

Building scored ratings across millions of organizations requires internet-wide collection infrastructure. Sourcing that data layer from Deepinfo lets the customer's engineering team focus on the scoring model, not on the observatory.

The challenge.

Building scored ratings across millions of organizations requires continuous internet-scale data collection: domain corpus, certificate transparency monitoring, DNS state, port scanning, web fingerprinting, CVE enrichment. Doing this at quality requires a multi-year engineering investment focused on collection infrastructure rather than core product.

The workflow change.

The customer integrated Deepinfo's API and Data Feeds into their scoring pipeline. Rather than building a parallel internet observatory, the engineering team consumes structured data directly: subdomains, certificates, DNS records, technology fingerprints, CVE enrichment, and focuses internal effort on the scoring model and customer-facing product.

The outcome.

Engineering velocity on the core product increased substantially as the team stopped maintaining internal collection infrastructure for capabilities Deepinfo already operates. Coverage expanded; freshness improved; the rating product gets better data without internal team having to build the data layer.

WHAT CHANGED

Concrete outcomes for the product team.

  • Source data layer for the product: comes from Deepinfo APIs and Data Feeds.
  • Engineering team focused on scoring model: rather than internet observatory infrastructure.
  • Coverage and freshness: match what Deepinfo's own platform provides.
  • Time-to-add new data signals: to the product reduced as Deepinfo's roadmap aligns with rating signal needs.
  • Cost and complexity of internal collection infrastructure avoided: at the multi-year engineering investment scale.
BUILDING WITH THE DATA

See what you can build on the dataset.

Talk to us about Data Feeds and APIs. The same dataset that powers Deepinfo's platform powers third-party products built on top of it.

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