From PHI-compliant pipelines to anomaly-tagged sensor logs, our teams have delivered AI-ready data for enterprises where compliance and accuracy are non-negotiable.
Our synthetic pipelines simulate rare or regulated scenarios, helping clients de-risk fine-tuning and expand model coverage without real-world exposure.
We’ve labeled hundreds of thousands of instruction-following, multi-turn prompts — with workflows optimized for privacy, consistency, and reuse.
We track every transformation, label, and enrichment layer — creating datasets that are easy to validate, retrain, and extend across LLM versions.
AI systems require data that’s structured for training, validation, and inference — not just analysis. That means high signal-to-noise ratios, task-specific formatting, and traceability for every transformation or label.
Yes. We build pipelines for tabular data, text, images, audio, and long-form inputs — including streaming, time series, and conversational data used in GenAI applications.
Synthetic data helps simulate rare events, fill edge-case gaps, or anonymize sensitive records. We design generators with fidelity controls, annotation consistency, and traceable metadata.
We embed privacy-preserving techniques into the data pipeline — including PHI masking, lineage logging, and access control. We’ve delivered HIPAA- and GxP-compliant data pipelines at scale.
Absolutely. We’ve deployed human-in-the-loop and model-assisted workflows to label multi-turn, instruction-following, and long-form prompts — including clinical and regulated use cases.
We assess your dataset for completeness, diversity, prompt structure, versioning, and auditability — and can help you build filtering, enrichment, and formatting pipelines to close the gap.
