Tracer AI, a private retrieval

Tracer AI, a private retrieval and asset-intelligence platform that turns large codebases, documents, operational data, and knowledge stores into ranked, explainable AI context. I architected the ingestion, classification, scoring, vector retrieval, graph relationships, review and promotion workflows, plus Runtime-C context bundles. Tracer supports tenant-scoped retrieval with citations and keeps source systems read-only.

KEY CAPABILITIES • Source ingestion and warehouse scanning • Content classification and asset scoring • Fingerprinting, tagging, and duplicate detection • Vector retrieval with lexical/entity reranking • Knowledge-graph relationships and dependency mapping • Review, approval, rejection, and golden promotion workflows • Explainable citations and bounded Runtime-C context bundles • Tenant-scoped retrieval and source-system isolation • Read-only operation against source CRM/data systems

ARCHITECTURE FLOW Raw Sources → Scan → Classify → Score → Fingerprint → Inspect Content → Tag → Review → Approve / Reject → Vector / Graph / Corpus → Tracer Search → Context / Resource Bundle → Runtime-C