Tracer AI
AI-Enabled Retrieval and Verification Layer
Hybrid search and evidence retrieval for private databases, software repositories, CRM objects, files, and business records.
Overview
Tracer AI is the retrieval layer underneath the product stack. It combines vector search, graph relationships, metadata, ranking, source materialization, and artifact receipts to produce evidence-backed answers. Instead of asking an LLM to guess from memory, Tracer retrieves ranked source material first, attaches provenance, and returns small verifiable artifacts that can be inspected or linked back to the original record. This is the anti-hallucination wedge: not a promise that AI can never be wrong, but a system design that reduces unsupported answers by forcing retrieval evidence, source IDs, graph context, and artifact references into the response path.
Core Workflow
Operating Philosophy
This product follows the same platform pattern used across the XYZ Labs stack: define stable primitives, attach relationships, retrieve evidence, generate artifacts, and make the workflow repeatable. The system is designed to be modular enough to deploy alone, but strongest when paired with the rest of the stack.
Core Components
Differentiators
Hybrid vector + graph retrieval, evidence receipts, source traceability, private database search, AI-enabled but not AI-dependent, supports normal search bar or agent workflow integration
Roadmap
Near-term work focuses on hardening persistence, improving documentation, polishing demo workflows, adding screenshots, and packaging clean deployment snapshots. Future versions can add deeper automation, richer UI skins, advanced scoring, multi-tenant governance, and expanded integrations.