You tell us what your model must do. We define the schema, distribution and acceptance criteria first, generate to that spec, and return data verified against it.
Book a scoping callSingle-turn and multi-turn examples for supervised fine-tuning, with controlled difficulty, tone and format.
Questions and reference answers generated from your documents, each linked to the passage that supports it.
Tool-calling traces and multi-step workflows, including failure and recovery paths, validated against your tool schemas.
Structured-output and labeling data with a fixed label schema and measured label accuracy.
Hindi and Hinglish data with native-speaker review. Other languages are scoped per project once reviewer coverage is confirmed.
Rare, ambiguous and hostile inputs that real traffic produces too seldom to train or test on.
Grow a small set of real or expert-written examples into a larger one, the approach popularized by Self-Instruct, with similarity filtering to drop near-copies.
Systematically combine user types, intents, constraints and difficulty levels so coverage is designed rather than left to chance.
Generate from source documents and keep the link to the supporting passage, so factual claims can be verified.
Over-generate, score against a rubric and keep only what passes, rather than shipping everything the generator produced.
Thresholds depend on your task and risk tolerance, so we set them with you during scoping and report against them at delivery. A dataset that misses them is revised, not shipped.
Synthetic datasets and evaluation benchmarks for teams shipping LLMs and AI agents, delivered with the evidence.