See design-ins forming. Catch the sockets rivals leave open.
Trust your account data.
Public traces that engineers are evaluating parts or tools - GitHub, forums, FCC filings, job posts - surfaced, scored, and linked to a source.
Every account arrives worked: researched, evidence linked, a suggested play attached — and the data underneath it (names, titles, emails, accounts) deduped and corrected in your CRM. Your FAEs spend their hours in conversations, not in tabs. The judgment calls stay human — by design.
What's moving in your market — new releases from rivals, new entrants in your category, and the EOL/PCN notices that open sockets you can chase. Open web, refreshed on a cadence.
Three steps, each de-risking the next — we test against your products first, and only scale what works.
The start is narrow: one product or line. Fit + displacement signals, or a named map of your addressable market — ranked, scored, linked to a source.
→ you see real signals before committing to anything bigger
The test worked — now it runs across everything you sell. Full catalog mapped, installed-base + displacement report, the market map, and the signal feed set up and tuned.
→ the whole catalog, not a cherry-picked sample
Always-on operation: EOL/PCN monitoring, new signals routed to your team, market intel refreshed on a cadence.
→ signals keep arriving without you staffing for it
Fewer signals, every one verified — and the judgment stays with you.
One trace is noise. A GitHub thread could be a student. A job post could be routine backfill. An FCC filing could be legacy.
A signal is what survives correlation: the thread, plus the job post naming the same part family, plus the filing from the same company — inside the same window. Correlated, scored, verified by a human, linked to a source you can open.
Below the bar, you never see it. A handful of accounts worth a call beats a thousand rows.