When an auditor, a customer, or a regulator asks “where did this model come from?”, most teams cannot answer cleanly. The AI Model Lineage Tracker builds a structured record of every model in your stack — its base model, fine-tuning sources, training-data provenance, and dependency chain — so the answer is one document away.
How it works
You add each model as a row: its name and version, the base model it builds on, any fine-tuning source and data, and the provenance of the training data (public, licensed, customer, or synthetic). You can mark dependencies — which deployed models call or build on others — and the tool renders the lineage chain.
The output is a clean, copyable lineage document. It maps onto the provenance and data-governance questions in the EU AI Act’s technical-documentation requirements and the “training data” and “model details” sections of a standard model card.
What it captures
For each model: name and version, base model, fine-tuning source, training-data sources with licensing status, and downstream dependencies. The tool flags rows where training-data provenance is left as “unknown,” because unclear provenance is one of the most common copyright and compliance risks in an AI supply chain.
Tips and notes
The highest-value field is training-data provenance — it is the one most teams cannot reconstruct after the fact, so capture it while you still can. The second is version history: record the version of each base and fine-tuned model, because “we use GPT-class model X” without a version is not auditable. Treat the lineage record as living documentation and update it whenever you swap a base model or retrain. Everything is assembled in your browser and nothing is uploaded.