Email Verification + Domain-Success Prediction
Rewrote in-lookup email verification to run in parallel and prioritize the right candidates first, plus a full AI domain-success tracking system. Doubled prediction success and drove lookup success from ~55% to ~90%.
The single largest revenue lever I’ve moved at RocketReach.
Problem
Email verification is the most expensive and most failure-prone step in the lookup pipeline, and the lookup pipeline is the core product. Two compounding problems by 2024:
- Verification ran sequentially on a candidate list, so the lookup waited on the slowest probe.
- Domain-success prediction — given an unknown email at a known domain, will it land? — was based on legacy heuristics that aged badly as the email-deliverability landscape shifted.
Both ate directly into the lookup-success rate, which is the lever for credit consumption and revenue.
Design
The verification rewrite parallelized probes across candidates and added an explicit prioritization that ran the candidates most likely to succeed first, short-circuiting the rest of the wave the moment one landed. Throughput accounting moved into the call path itself so backpressure and cost were continuously controlled.
The domain-success prediction layer was rebuilt as an AI-driven tracking system that ingests outcomes per domain, models drift, and re-prioritizes candidate strategies as deliverability shifts. This piece sits behind every email lookup the product makes.
Outcome
- Prediction success roughly doubled.
- Lookup success rate moved from 55% at the start of 2024 to ~90% by end of 2025.
- That’s the largest direct multiplier on credits-used and revenue I’ve delivered. The size of the lever and how directly it shows up in the company’s P&L is why it ranks ahead of cleaner systems projects on this list.