Behavioral data infrastructure for frontier AI teams deploying autonomy, manipulation, and voice AI beyond the Western data envelope.
The consequences are not theoretical. They are disengagement events, manipulation failures, and transcription errors happening now in production.
A Western-trained AV stack encounters 3.2× more disengagement events in dense, mixed-traffic markets. Unprotected left turns through six-way unsignalized intersections. Two-wheeler swarms. Cattle mid-intersection. Monsoon-degraded LiDAR. These are not edge cases in Bengaluru — they are the standard operating condition.
AV · Multi-city · Multi-conditionThat figure — IBM Research, 2025 — is the binding constraint on the humanoid deployment wave. Gas stoves, dupattas, kirana shelves. Every home environment that is not a California kitchen is an environment humanoid robots have never seen. Capital is there. Models are advancing. The data is not.
ROBOTICS · Indian homes · Tier 2/3Code-switched speech — Hinglish mid-sentence — breaks models never trained to expect it. A healthcare ASR system cannot mishear a Bhojpuri patient's symptoms and still be deployed in rural primary care. The audio exists. It does not exist in any licensable, annotated, provenance-clean corpus.
VOICE · 22 languages · 100+ dialectsThis is not a quality problem. It is a supply chain problem. Birha is the supply chain.
No signals, no lane discipline, constant multi-agent conflict.
AV · Multi-city · Multi-conditionGas stoves, dupattas, kirana shelves — none of it in your training data.
ROBOTICS · Indian homes · Tier 2/3Cars, buses, trucks, autos, bikes, and informal interactions.
AV · Multi-city · Multi-conditionHinglish mid-sentence. Bhojpuri healthcare. ASR breaks here.
VOICE · 22 languages · 100+ dialectsThree verticals. One pipeline. IAA-verified on every delivery batch.
Five stages. Every one verified. This is how behavioral data becomes deployment-ready training ground truth.
LiDAR point clouds reveal clutter, actors, and ambiguity.
This is where edge-cases begin: noisy geometry, mixed motion, and uncertain context.
Sensor fusion prioritizes what matters in real time.
The sweep maps confidence around the ego vehicle so reaction decisions are grounded, not guessed.
Contributors receive a dataset brief, not a free-upload prompt.
Every clip is scenario-specified, on-device QC'd, and cohort-verified before annotation begins.
Multi-pass annotation scored with Fleiss kappa on every delivery batch.
Disagreement is surfaced, not averaged. Your model decides how to weight ambiguous instances.
Dataset cards, IAA reports, provenance chain — delivered with every batch.
The same dataset licensed to multiple buyers at near-zero incremental cost.
01 — Collection
Contributors receive a scenario brief, not an upload window. Every task is specified — time of day, road type, minimum vehicles in frame. On-device QC before any clip enters the pipeline.
02 — Annotation
Multi-pass human review with Fleiss kappa scoring on every delivery batch. Batches below threshold go to expert re-review — not to delivery. Disagreement is shipped with the data, not hidden.
03 — Delivery
Dataset cards, IAA reports, and provenance chain included with every batch. API delivery. HuggingFace-compatible format. The same dataset can serve multiple buyers — incremental cost approaches zero.
Dataset access reviewed within 48 hours.
Most training datasets describe the world AI was built in. Birha captures the world it's being deployed into — Indian roads, Indian homes, Indian dialects. Task-engineered collection, not open uploads. IAA-verified annotation on every delivery batch.
All three verticals are in active collection and available for licensed access.
Custom collection sprints available for edge cases not in the pre-built library.
India isn't just a market. It's a stress test for autonomy.
If your model works here — it works anywhere.
87% of AV training data comes from three geographies. None of them are here.
KITTI has 8 classes. nuScenes has 23. Bridge-v2 has 25 environments. IndicSUPERB aggregates dialects into a single distribution. Every one of these datasets was built for a world your deployment is not in. Birha builds for the world it is in — and licenses it to the teams who need it.
Enterprise inbound reviewed within 48 hours.
Production workflows trained for high-entropy environments.
Production workflows trained for high-entropy environments.
Production workflows trained for high-entropy environments.
Production workflows trained for high-entropy environments.
Get Access
Tell us the vertical, the failure mode, and the coverage gap. We'll match it to our catalog or scope a custom collection sprint — response within 48 hours.
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No spam. Enterprise inbound reviewed within 48 hours.
