Training data built by experts, filtered by machines.
The data is the model. We design and run data programs that turn your experts’ judgment and your operational history into training signal.
Every successful fine-tune we have shipped was won in the data phase. The training run itself is a few GPU-days; the dataset behind it represents weeks of careful decisions about what to include, how to label it and how to check it. We treat data as an engineering discipline with versioning, tests and documentation.
Most enterprises already own the raw material: years of tickets, documents, decisions and corrections. We mine it inside your environment, redact sensitive fields, and identify where human labels are still needed. For those, we run annotation programs with vetted domain experts — clinicians, lawyers, actuaries, accountants, engineers — under guidelines we write and iterate with your team. Where coverage is thin, we generate synthetic examples and filter them aggressively.
Signs you need training data.
The right technique, chosen by the eval.
- Log mining. Extract and reconstruct training pairs from historical system and human outputs.
- Expert annotation. Guideline design, calibration rounds, double-labelling and adjudication.
- Synthetic data. Teacher generation with verifier and judge filtering, diversity controls and deduplication.
- Data QA. Agreement tracking, contamination checks, and per-slice coverage reports.
- Versioned datasets with lineage
- Annotation guidelines and rater statistics
- Data card documenting sources, filters and known gaps
- Reusable data pipeline in your environment
All assets transfer to you at handover. No licence fees.
Questions buyers ask us
Who are your annotators?
Vetted professionals in the relevant field, working under NDA, managed by our data leads. For the most sensitive data, we train and calibrate your own staff instead.
Is synthetic data safe to train on?
When filtered properly, yes. We verify synthetic examples with programmatic checks and calibrated judges, deduplicate against real data, and never let synthetic data near the evaluation set.
How do you handle PII?
Detection and redaction run inside your environment before any data reaches annotation or training, and access is limited to named personnel.
Bring us the task. We'll bring the model.
A 45-minute technical briefing with a senior research engineer. You leave with a feasibility read, a data plan and a fixed quote — whether or not you hire us.
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