Experiment lineage for ML team research velocity
Turnkey experiment tracking and artifact management system purpose-built for ML teams to version and query model runs, preprocessed datasets, and hyperparameter decisions without breaking their existing tools.
The problem
ML engineers and data scientists waste several hours per week reconstructing what they tested, why they rejected it, and how to reproduce results. They spread knowledge across Slack, scattered notebooks, local directories, and ad-hoc spreadsheets. Onboarding new team members to past research is slow; collaboration on long-running projects involves manual reconciliation of conflicting experiments; and regulatory/audit requests for model lineage are answered via email chains instead of a queryable system.
Who has it: Mid-market tech companies and enterprise data science teams (50–500 ML engineers) running unstructured experimentation on proprietary datasets, not yet ready for full MLOps platforms.
Why now: LLM fine-tuning, RAG pipeline tuning, and multimodal model experimentation have exploded across mid-market tech and enterprise ML teams. Existing MLOps platforms (MLflow, Weights & Biases) require weeks to integrate and are overkill for teams still in exploration mode. Regulatory pressure on AI auditability (EU AI Act, FTC enforcement) is forcing teams to document lineage; most have no system to do it.
Where this came from
2 public sources behind this idea.
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