# Turn real behaviour into data

> Canonical: https://www.overmindlab.ai/product/data-workshop

*Data Workshop*

Your production traces are already the best training data you have. Overmind turns them into training and eval data: selected, audited, and redacted, with a profile that configures the training run that follows.

## From traces to training-ready

Your agents already generate the best training data you will ever have. The Workshop makes it usable.

### Build datasets from real runs

Select production traces and promote them into a dataset. Filter by agent, eval score, or time window so only the runs worth learning from make it through. Every example keeps a link back to its source trace.

`Data you already own` `Best runs only` `Full provenance`

### Audited by detectors, then by an agent

Deterministic checks catch duplicates, malformed turns, token overflow, and PII in seconds. Then a coding agent reads the corpus for what rules miss: label conflicts, answer leakage, degenerate outputs. Every fix it proposes is verified in a sandbox first, and applying one is a single click that lands as a versioned commit.

`Detector and agent passes` `Fixes verified before shown` `Every apply is a commit`

### The profile configures the training run

The Workshop profiles shape, balance, and token distributions, then turns them into a recommended training configuration: model tier, epochs, learning rate, and adapter settings, with cost and duration estimated before you commit. PII is detected and redacted so what leaves the Workshop is safe to train on.

`Recommended configuration` `Cost known upfront` `PII redacted`

## What you get

Curation, validation, and profiling in one place, wired into evals and training.

### Curation

- **Trace selection** — Bulk-select runs filtered by agent, score, and time. Choose the agent surface or raw model call.
- **Provenance tracking** — Every example links back to its source trace, so any training row can be audited to the run that made it.
- **Dataset history** — Every applied fix is a commit that records the score it produced. Restore any point in the history.

### Validation

- **Structure checks** — Validates turn order, tool-call shape, declared-tool usage, and token overflow before training can start.
- **Agent-fit checks** — Tool overlap, input-key match, and provenance checks so the dataset matches the agent it will train.
- **Quality pillars** — Weighted scores across integrity, signal, coverage, uniqueness, safety, and fit. Hard failures block readiness.

### Profiling and safety

- **Token distributions** — Input and output token totals that set context length and filter the model catalogue to what fits.
- **PII detect and redact** — Names, locations, organisations, emails, and phones are labelled and masked before training.
- **Hyperparameter recommendations** — Epochs, learning rate, and adapter settings recommended from dataset stats, carried into training.

## Your traces are your dataset

Curated, validated, profiled. Ready to train on.

- [Build your dataset](https://console.overmindlab.ai)

- Book a call: https://www.overmindlab.ai/contact