Generate training and eval data without refusals.
Fine-tuning pairs, eval sets, adversarial corpora — through the same OpenAI-compatible API your pipelines already use.
ML teams need realistic, controlled data — including data that triggers off-the-shelf model refusals. Policy Gateway sits in front of less-restricted inference and lets you govern generation per project, with structured outputs and decision logs on every example.
Why teams in synthetic data hit a wall.
Refusal-tuned models can't generate adversarial data
Training a safety classifier? You need examples of unsafe prompts. General-purpose APIs refuse to write them — leaving you with hand-curated datasets that don't scale.
Generation quality drops at scale
Off-the-shelf APIs apply unpredictable refusal rates that vary by topic and even by phrasing. Reproducible large-batch jobs become impractical.
No governance audit on what you generated
When a fine-tuning dataset ships into production, you need provenance: which policy, which prompts, which model. Most generation APIs offer no decision metadata.
Built for synthetic data workloads.
Less-restricted inference, your rules
Generate the prompts and completions you actually need for training. Your policy decides what's in scope — not the provider's defaults.
Structured JSONL output
Generate fine-tuning pairs, eval entries, or labeled corpora directly in the format your training pipeline expects. No post-processing required.
Per-project quotas and key scoping
Issue a scoped key per dataset job. Track generation volume, cost, and decision history per project — and prove dataset provenance to your reviewers.
Scenarios from the field.
Eval set generation
Generate 10k labeled prompts for testing a safety classifier. Track every example with policy ID and reason code so QA can replay decisions.
Fine-tuning pair creation
Produce instruction/response pairs for vertical model fine-tuning. Same governed API; no refusal noise polluting your dataset distribution.
Adversarial training data
Generate jailbreak attempts and edge cases for safety training. Controlled, audited, reproducible — and isolated to the project key that paid for it.
Designed for the frameworks your auditors care about.
Built so your dataset shipping reviews don't stall on questions about provenance.
- Decision metadata per recordPolicy ID, reason code, and key scope on every generated example.
- Reproducible runsSame prompt, same policy version → comparable output across batches.
- Per-project quotasHard caps so a dataset job can't blow the budget.
- JSONL-ready outputsStructured straight into your training pipeline.
- Zero data retentionGenerated content not used for training or shared.
- SOC 2 (in progress)Enterprise audits underway.
Ready to bring governance to your synthetic data stack?
Talk to an engineer about your deployment, or grab an API key and start building today.