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ip-intelligence-internal.mdIPinfo · Nov 2025–June 2026

IP Intelligence Data: Internal Initiative

ShippedTeam: Data + Infra + Web6 months
TL;DR

Proposed and launched a more precise layer of IP intelligence data, using a private to public beta process to validate market fit and production readiness before full GA.

Outcome

  • ↑Data accuracy, validated through structured biweekly beta testing
  • ↑Beta enrollment, with a smooth self-serve scheduling flow
  • ↓Time to resolve tester-flagged issues, via tight validation loop
  • ↑Organic discovery by existing customers, no dedicated marketing push
  • ↓Time from discovery to access, fully self-serve and automated within 24 hours
Saw It
  • Existing IP data gave a location, but not precise enough placement, with no specific customer request driving the work.
  • Open questions: was this actually a market priority, and did higher-precision location data raise PII-adjacency concerns.
↓
Figured It Out
  • Ran customer interviews to confirm use case and business value before committing further investment.
  • Proposed moving to private beta with the data as-is rather than waiting to perfect it, to get real signal sooner.
  • Selected 5 handpicked beta testers with full data access and biweekly validation cycles.
↓
Did It
  • Defined the private beta to public beta launch process end-to-end, resolving accuracy and categorization issues as testers surfaced them.
  • Investigated the compliance implications of higher-precision location data, confirming IP addresses are PII-adjacent but not classified as PII, clearing the path to proceed.
  • Partnered with the website team to launch a public-facing site to showcase the data alongside the public beta.

My Role

  • Defined the private beta to public beta launch process end-to-end.
  • Led discovery and customer interviews, explored packaging concepts, designed the beta program.
  • Used AI tools to speed up data grouping and categorization, making the dataset easier to digest for internal stakeholders
  • Used AI to accelerate competitor landscape research
  • Used AI to quickly prototype landing page design concepts, feeding directly into the UX and web team's build
  • Used my data science background to validate categorization, deduplication, and data quality issues directly with engineering.
  • Partnered with the website team to build a public-facing site to showcase the data.
  • Personally investigated the compliance implications of higher-precision location data, confirming IP addresses are PII-adjacent but not classified as PII, clearing the path to proceed
  • Partnered with a UX designer and the website team to build a public-facing site to showcase the data
  • Partnered directly with Sales throughout the beta: the public website functioned as their proof point in enterprise conversations, turning product discoverability into a direct sales asset rather than a side effect

Engineering Collaboration

  • Ongoing back-and-forth throughout the testing period to resolve accuracy and categorization issues as testers surfaced them.
  • Regression and pipeline improvements.
Full breakdown

Problem & Risk

Value RiskFeasibility RiskCompliance Risk
  • Existing IP data gave a location, but not precise enough placement.
  • No specific customer request was driving this. Open questions were whether this was actually a market priority, and what the real usage would be.
  • No clear existing playbook for how to package this data internally at the needed precision.
  • Higher-precision location data raised the question of PII-adjacency

Customer Discovery & Validation

Evidence
  • Ran customer interviews to confirm the use case and business value before committing further investment.
  • Selected 5 handpicked beta testers with full data access, biweekly validation cycles.
  • Validated categorization and deduplication issues directly with users, confirming when fixes had actually resolved reported problems.
  • Prioritized and escalated issues based on relevance rather than treating every report as equal.

Context

  • Project already in progress.
  • Proposed moving to private beta with the data as is rather than waiting to perfect it further, to start getting real signal sooner.

Timeline

git log --oneline⏱ ~6 months to GA
kickoff: proposed private beta with existing, unperfected dataLate 2025
onboard: 5 handpicked beta testers, biweekly validation cycle beginsEarly 2026
iterate: engineering resolves accuracy and categorization issuesEarly–Spring 2026
ship v1: public beta launched, paired with new customer websiteSpring 2026
full GA: general release following public beta periodNext
~ rough sketch, not an actual screenshot, beta program stages ~

private beta

5 handpicked testers

validation cycle

biweekly, ~1 month

public beta

launch with website

full GA

general release

~ rough sketch, not an actual screenshot, public beta website concept ~
www
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Who Benefits

  • Customers get clearer, more accurate IP intelligence data.
  • Customers already using the core product had a low-friction path to discover and try the new one, no outreach required.
  • Sales got a validated playbook and product early, using the website as a live showcase of data quality to support enterprise deals.
  • Internally, this was the first time running this private-to-public beta lifecycle, building a repeatable muscle for future product launches.

Next Steps

  • Full GA launch following the public beta period.