Know exactly where your data comes from — and what breaks when it changes.
TraceMap is an AI-powered, column-level data lineage platform. Autonomous AI agents read your code and metadata, map how data flows across every system down to the column, and keep that map continuously accurate — verified before it's ever trusted.
The problem
Why lineage is broken today
Most lineage tools were built for a world of static schemas and a handful of connectors. Modern data stacks outgrew them years ago.
Brittle, hand-coded parsers
Traditional lineage tools rely on parsers written for specific SQL dialects and tools. One schema change, one new framework, and coverage silently breaks.
Months of setup before any value
Standing up legacy lineage means mapping every connector and configuration by hand — long implementation projects before a single question gets answered.
Graphs only engineers can read
When lineage lives in a dense node graph, the compliance analyst or business owner who needs the answer has to find an engineer to get it for them.
An agentic platform, not a pipeline
Traditional lineage tools are static pipelines. TraceMap is a team.
Static pipelines break the moment your stack changes. TraceMap is a coordinated team of specialized AI agents that continuously discover, understand, verify, and improve your lineage — with humans approving anything the AI isn't sure about.
Meet the five agents behind every answer TraceMap gives you.
- 01
Discovery
Autonomously maps your data landscape — warehouses, repositories, BI tools — and proposes its own configuration for your approval. Onboarding in days, not months.
- 02
Understanding
Reads SQL, Python, and pipeline code like an engineer would, extracting column-level lineage from any language or tool — no hand-built parsers, no connector backlog.
- 03
Reasoning
When code alone isn’t enough — dynamic references, ambiguous mappings — it gathers the surrounding context needed to resolve the answer instead of guessing.
- 04
Verification
Every proposed lineage edge is independently checked against your live schemas before it’s accepted; anything uncertain is routed to human review with the AI’s reasoning attached.
- 05
Learning
Every human correction makes the platform smarter automatically; accuracy compounds over time without vendor intervention.
How it fits together
One platform, read-only in, verified answers out
Diagram A — Ecosystem overview
Generic source categories connect read-only into TraceMap, which produces an interactive graph, conversational answers, and a review queue.
Built for every role
One source of truth, three ways to use it
Days to first lineage graph, not months
Autonomous discovery replaces hand-configured onboarding.
Every edge verified before it’s trusted
Checked against your live schemas, not just inferred from code.
Zero hand-coded parsers
AI reads your code and metadata directly, in any language.
Column-level precision
Not table-level guesswork — the exact field, every time.
AI you can audit
This is the “gates” in Reason Gates
TraceMap treats AI output as a hypothesis until it's verified — schema-checked, confidence-scored, and routed to human review when uncertain, with provenance on every relationship it proposes.
Runs in your cloud
TraceMap deploys inside your own cloud environment.
Bring Your Own Model
Use your own model so code and metadata never leave your environment.
Strictly read-only
Every data-source connection TraceMap makes is read-only, by design.
Verified before accepted
Nothing is presented as fact until it is checked against your live schemas.
See your data's lineage, verified end to end.
Get a tailored walkthrough of TraceMap on data that looks like yours.
Prefer email? hello@reasongates.com