STACK Lineage
Product · AI Data Provenance

From raw log to model weight, auditable end-to-end.

STACK Lineage tracks every byte that influenced a deployed model — raw source, cleaning step, feature, embedding, fine-tune — and emits EU AI Act Annex IV documentation on demand as signed verifiable credentials.

1d
Auditor Signoff Time
8wks
Beats Manual Workflow
Annex IVready
EU AI Act Output
100%
Training Byte Coverage
Capabilities

Regulators ask what data trained this model. Have the answer ready.

EU AI Act, NIST AI RMF, ISO 42001, and the Colorado AI Act all demand training-data provenance. Lineage produces it as a queryable, signable, auditor-ready artifact.

Source-to-Weight Graph

Every training run links to its input data sets, feature transforms, eval results, and reviewer signoffs.

Cleaning-Step Provenance

Each PII redaction, dedup, filter, and synthetic-data injection recorded as a verifiable transformation step.

Embedding Lineage

Vector store contents linked back to original document, ingest run, and license tier.

Auditor Export

Annex IV / NIST AI RMF / ISO 42001 evidence packs generated as W3C Verifiable Credentials. Signed, time-stamped, immutable.

Drift to Approved State

Detect when production model's effective training data differs from the signed baseline — silent re-training caught.

Multi-Tenant Scoping

Per-customer model lineage isolated and exportable separately. Defense and FSI deployment patterns supported.

Frequently Asked

Questions teams ask before deploying

Straightforward answers about scope, integration, data handling, and rollout.

Do you replace Weights & Biases or MLflow?

No — we integrate with them. They track experiments. We track regulatory provenance with cryptographic chain-of-custody.

EU AI Act applicability?

High-risk system Annex IV technical documentation, foundation-model GPAI documentation, plus the conformity-assessment workflow — out of the box.

How is data captured?

Hooks into your ingest pipeline (Beacon, Airflow, Spark, Snowflake), feature store (Tecton, Feast), and training framework (HuggingFace, PyTorch, JAX). One-line instrumentation.

On-prem and air-gapped?

Yes. Verifiable credentials signed offline; transparency log can be a private Rekor instance. Air-gapped via offline-signed import.

Ready to See It Live

Ship Annex IV docs by Friday

Bring one production model and one training run. We'll generate the signed evidence pack your auditor would accept.