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11 persona navigation hubs mapping the engineering-signal panel to specific buyer roles.
Each persona page covers the workflows, quick-start paths, and honest caveats for one Marcus 100 buyer role. Pick the page that matches your role and start from the recommended quick-start bookmarks.
Scout acquisition targets via the engineering-acceleration signal — 3 to 6 weeks before the round closes and the price hardens.
Corporate Development directors at hyperscalers (Microsoft, Google, Cisco, Salesforce, Adobe, Oracle, IBM, Snowflake, Databricks) and Fortune 500 incumbents face a structural sourcing problem: by the time an acquisition target circulates a banker book, multiple competing strategic buyers already know about the asset. VC Deal Flow Signal surfaces engineering-acceleration patterns 3 to 6 weeks before fundraise announcements — early enough to start exploratory conversations before pricing tension forms. This page maps your specific Corp Dev workflows to the public pSEO surface we publish.
Scout bolt-on targets and benchmark portfolio company engineering velocity through one unified signal panel.
PE Operating Partners at growth-stage funds (Vista, Thoma Bravo, KKR, Bain Capital, EQT, ICONIQ) face two recurring problems: sourcing bolt-on acquisition targets that fit existing platform investments, and benchmarking portfolio company engineering velocity against public peers. VC Deal Flow Signal addresses both via a single public engineering-acceleration signal panel. This page maps your specific Operating Partner workflows to the pSEO surface we publish.
Vendor consolidation scouting and competitive engineering benchmarking through one unified signal panel.
Non-engineer tech VPs (VP Engineering, VP Platform, Chief Architect, VP Infrastructure) at enterprise companies face two parallel problems: which vendors should we consolidate around as we cut budget, and how does our engineering investment compare to publicly observable peer companies? VC Deal Flow Signal addresses both via the same engineering-acceleration signal panel — vendors with sustained acceleration are likely to consolidate the category; peer companies with relative acceleration suggest where your team may be underinvested.
Source pre-round deals from public engineering signals and differentiate from established-fund sourcing motions.
Emerging-manager VC funds face a competitive sourcing problem: established funds have larger sourcing teams, more partner relationships, and broader access to the warm-intro network. Code-side sourcing — the practice of identifying companies before the round circulates by reading their public GitHub activity — is the rare sourcing channel where being smaller and faster is the structural advantage. VC Deal Flow Signal publishes the public signal panel that makes this sourcing motion repeatable.
Map your competitive landscape and identify investor targets aligned with your sector through public engineering signals.
Founders face two sequential sourcing problems: understanding the competitive landscape before raising (which companies are 6-12 months ahead in your sector?) and identifying investor targets whose thesis matches what you're building. VC Deal Flow Signal publishes the public engineering-signal panel that makes both clear — and lets you bring evidence to investor conversations rather than just claims.
Open dataset, published methodology, citable SSRN paper, and APIs designed for academic and policy research.
Academic and policy researchers studying venture finance, technical innovation, or engineering-organization dynamics have a recurring data access problem: most relevant data is proprietary and gated by Crunchbase, PitchBook, or CB Insights. VC Deal Flow Signal addresses this by publishing the full engineering-acceleration signal panel under CC BY 4.0 — the panel itself, the underlying methodology, and the SSRN paper documenting empirical findings are all freely citable.
Citable, independent, public-data sourced engineering-acceleration signal for venture-story reporting.
Tech journalists and industry analysts (TechCrunch, Information, Axios, Bloomberg, Reuters, plus boutique research shops) face a recurring problem: most venture-stage data is proprietary and gated, and the embargoed data flowing from PR shops is partial. VC Deal Flow Signal addresses this with citable, independent engineering-acceleration data sourced from public GitHub events — no NDAs, no embargoes, no privileged access required.
Spot pre-seed breakouts 3-6 weeks before the round gets crowded — using public GitHub commit velocity as the earliest engineering-momentum signal.
Angel investors need signals before the round gets crowded. Commit velocity is the earliest public indicator of engineering momentum — visible weeks before pitch decks circulate, press hits, or SEC filings appear. VC Deal Flow Signal tracks 400+ startups across 20 sectors, refreshed weekly.
Use GitHub engineering acceleration to surface deals before the partners you scout for do — with a reproducible, data-driven sourcing edge.
Scouts live on speed and pattern recognition. The startups that show engineering acceleration today are the ones partners see decks for in six weeks. VC Deal Flow Signal gives scouts a reproducible, data-driven sourcing edge — free, with an MCP server that plugs directly into Claude Desktop for natural-language deal screening.
Level the playing field against established funds with a single dataset that surfaces breakout startups across 20 sectors every week.
Solo GPs don't have a sourcing team. GitHub engineering signals level the playing field — a single dataset that surfaces breakout startups across 20 sectors every week, before the round is live. No expensive Bloomberg terminal, no analyst subscriptions. Just public code, measured.
Reproducible, defensible sourcing signals for investment committees — grounded in a longitudinal panel with an SSRN preprint and CC BY 4.0 dataset.
Family offices need reproducible, defensible sourcing signals — not vibes. VC Deal Flow Signal provides a longitudinal panel of GitHub engineering velocity, validated against 30 atomic findings in an SSRN preprint. Every metric links back to a public GitHub source.