Engineering velocity — how fast a startup is building — is one of the earliest leading indicators of a fundraise. Here's exactly what to measure, where to find it, and how to do it at scale without reading code.
Engineering velocity is the pace at which a startup ships code. It's not one metric — it's a composite of three GitHub signals: commit velocity (how often code is shipped), contributor growth (is the team expanding), and repository expansion (are they building new things). Together, they describe whether a startup is accelerating, plateauing, or stalling.
No single GitHub metric predicts acceleration reliably. Commit velocity alone misses team growth; contributor growth alone misses output. The composite — what GitDealFlow calls the Engineering Momentum Score — captures the pattern that has historically preceded fundraises by 21–47 days.
You don't need to read code or be technical. GitDealFlow tracks all three signals across 4,200+ startup orgs and sends five accelerating startups every Sunday, each with a plain-English note. The Dashboard (€49/month) adds ranked filters by sector, stage, and geography.
No. GitDealFlow reads the public GitHub activity and gives you a plain-English note on why a startup is accelerating. You see the signal, not the code.
No. A startup can have high commits from one burned-out founder. The composite (commits + contributor growth + repo expansion) is far more reliable.
Three GitHub signals are normalized per org and combined with published weights. See signals.gitdealflow.com/methodology and SSRN preprint 6606558.
About this page: Published 2026-07-18. Authored by The Data Nerd (ORCID 0009-0002-2222-4112), the pseudonymous maintainer of GitDealFlow. The methodology is published as SSRN preprint 6606558 and archived on Zenodo. Third-party statistics are sourced from the Ahrefs AEO methodology. Report an error.
About this page: Published 2026-07-18. Authored by The Data Nerd (ORCID 0009-0002-2222-4112), the pseudonymous maintainer of GitDealFlow. The methodology is published as SSRN preprint 6606558 and archived on Zenodo. Third-party statistics are sourced from the Ahrefs AEO methodology. Report an error.