Building a model?
Your data engine is ready.
High-quality, culturally-aware data and feedback — from multimodal annotation to RLHF & evaluation for SLMs, LLMs and everything in between.
AI Data Factory
AI is only as good as the data behind it. We run the AI data factory for you — from data collection to model-ready datasets and back again as your model learns.
It's the engine every service on this page plugs into: one loop, run continuously, not a menu of disconnected steps.
Collect
Source and capture the raw data your model needs, from field collection to multimodal recording.
Prepare
Clean, structure, and stage it against annotation guidelines built with you before labeling starts.
Annotate
Label at production volume, with accuracy defended at every batch.
Validate
Multi-level QA and golden-set checks before anything ships.
Deliver
Model-ready datasets, and a feedback loop that carries what your model learns back into the next cycle.
Physical AI
Robots and embodied agents learn from the physical world — and that data can't be scraped, it has to be captured. We run egocentric and physical-AI data collection with demographic diversity built in, consent-managed and audit-ready from day one, and annotate at sensor scale: 200–500K LiDAR frames a month.
That same collection and annotation infrastructure extends across the physical-AI surface — robotics, VLA models, embodied agents, human demonstrations, simulation data, and multimodal capture — ready to stand up when your program needs it.
Annotation Workbench
Image, video, text, audio, and LiDAR — all annotated by our in-house teams, not anonymous crowds. Detection, segmentation, tracking, transcription, and LLM prompt-response pairs; every project runs against annotation guidelines we build with you before labeling starts.
The work runs on Annotation Workbench, our labeling platform — where your guidelines are encoded and QA is built into the workflow rather than bolted on after it.
RLHF & Trust & Safety
Preference ranking, response rating, and structured human feedback that shapes how models behave — plus the trust and safety review that keeps them deployable. Judgment work, done by people trained to exercise it consistently. This is where model behavior gets shaped; measuring it is the job of evaluation.
LLM & Language Operations
India speaks in hundreds of tongues, and models trained only on English miss most of them. We build and QA language data across 22+ Indian languages — transcription, translation, prompt-response annotation, and locale-true content operations.
Model Evaluation & QA
A model that isn't measured isn't done. We run evaluation and QA as a standing function, not a final checkpoint — model evaluation, red-teaming, multi-level QA (L1/L2/L3), golden-set validation, and a feedback loop that turns every eval finding into better training data.
Evaluation coverage spans the modern surface: LLM evaluation, agent evaluation, benchmarking, hallucination testing, and AI safety testing — the same measurement discipline, pointed wherever your model needs to be trusted.
Building a model? The data engine is ready.
If your AI keeps breaking when it leaves the lab, let's build together.