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Step 1: Signal IngestionV1 Architecture

Turning Developer Activity into
Account Readiness

We don't guess. We use a deterministic Identity Graph to map raw developer signals to company domains.

The "Ghost User" Reality

GitHub gives you public activity, not a secret list of names. You can't "unhide" anonymous people beyond what is public.

However, we can map Org activity to Company Domains reliably. This allows us to build an account-level view of intent, even if the individual developer remains anonymous.

See the Identity Engine

Dive into the technical architecture of how we ingest, normalize, and match signals using Elixir OTP and Postgres.

View Architecture

Our Resolution Strategy

  • 1
    Ingest & NormalizeStandardize events from GitHub, Docs, and Packages into a uniform schema.
  • 2
    Org-to-Domain MatchingMap GitHub Orgs to verified company domains. This is our highest confidence signal.
  • 3
    Readiness ScoringCompute account stage based on the depth of activity, not just the volume.
Signal Processor v1
Sources: All Identity: Active

1. Live Signal Stream

github
2h ago
OPA Policy integration patterns
Discussion on enforcing compliance tags automatically during apply stage using OPA.
docs
4h ago
High-volume ingestion: Kafka connector
User viewing 'Advanced Configuration' for Stream Connectors > 5 mins.
pkg
1d ago
SDK Download Spike (Python)
v3.0.1 downloads increased by 400% from known IP block.

Signal Summary

Method: deterministic (v1)
Matched Account
Acme Financial(acme.io)
High Confidence
Rationale: Matched via GitHub Org verified domain
Plain English
"They are tightening controls to pass audits and reduce risk."
Business Value
Governance priority. Target CISO & Compliance leads.
Account Readiness
Evaluating (72/100)