Audit trail for every fine-tuning run.
Compliance sign-off in days, not months.
Cognify versions your datasets, training runs, and eval benchmarks so your compliance team has everything they need to approve a model for production — without slowing your ML engineers.
ML engineers ship. Compliance teams wait. Production stalls.
Fine-tuning a frontier model for regulated use isn't just an engineering problem — it's a documentation problem. W&B tracks your experiments. MLflow logs your runs. Neither produces a compliance-ready audit package that your governance team can actually sign off on.
6–12 weeks
— average compliance review cycle for AI deployments at regulated enterprises
Cognify compresses that to days.
From fine-tuning run to signed-off model
Connect your pipeline
One SDK call instruments your existing training scripts. Works with PyTorch, Hugging Face, JAX, and custom loops.
Version everything automatically
Every dataset snapshot, hyperparameter set, checkpoint, and eval result is hashed, timestamped, and linked into an immutable lineage graph.
Generate compliance packages
Export audit-ready documentation — model cards, data provenance reports, eval benchmark attestations — in formats your compliance and legal teams recognize.
Every control compliance teams ask for
Dataset versioning
SHA-256 hashed snapshots of every training and eval dataset, with diff tracking between versions.
Run lineage graph
Immutable directed graph linking data → training config → checkpoint → eval results → model artifact.
Auto-generated model cards
Structured documentation ready for internal review or external regulator submission.
Access control & approvals
Role-based sign-off workflows. Compliance team approves; ML team stays unblocked.
Eval benchmark tracking
Track performance on safety, fairness, and domain benchmarks across every training iteration.
Retention & archival
Configurable retention policies. 7-year archival for healthcare AI; 3-year for financial models.
Built for two audiences who usually don't agree
ML Engineers
- SDK integrates in one line — no pipeline rewrites
- Runs in your existing infra (GCP, AWS, Azure, on-prem)
- Open formats — no lock-in on your model artifacts
- CLI + Python SDK + REST API
AI Governance Teams
- Audit packages in PDF, JSON, and XLSX
- Immutable audit log — no one can edit post-sign-off
- Role-based approval workflows with e-signatures
- SOC 2 controls architecture (certification in progress)
What teams say
We were spending 8 weeks getting our fine-tuned model through compliance review. With Cognify, the audit package is ready before the review meeting even starts.
My team needed to know which training data went into every model version. Cognify's lineage graph became our source of truth for every compliance question.