POSTERIOR ◆ DATA INFRASTRUCTURE
We collect, annotate, and deliver multimodal training data from the physical world — egocentric video, speech, robot teleoperation, depth, and sensor data. Our hardware, our field teams, our daily routes, consented and traceable to the file.
01 / Dartum
Ground-level data collection at scale — capture hardware deployed on daily routes, every session validated at the source, annotated datasets delivered under a manifest traceable to the device, the contributor, and the consent form.
Calibrated capture hardware deployed to trained field teams on planned daily routes. Consent signed before every recording. Every session validated at the source — checksums, metadata, and hardware identity locked before anything leaves the device.
When drives return from the field, recordings are synced to cloud storage, then human annotation against your taxonomy with independent second review. Delivery carries a single manifest — SHA-256 hashes, consent codes, contributor registry codes per file.
02 / Data
Multimodal AI training data — consented, annotated, quality-gated, and delivered with per-file provenance.
Task-based videos, first-person and egocentric POV, fixed camera, multi-environment shoots. Resolutions from HD to 4K, frame rates to your brief, with bounding-box and action annotation.
Every category of 2D image collection — objects, scenes, documents to your specification — at whatever resolution and volume your brief demands, with annotation and preview.
Multilingual speech across 8 languages — English, Hindi, Kannada, Telugu, Tamil, Malayalam, Marathi, Sanskrit — scripted, natural, IVR, studio and field, with transcription to your schema.
Translation and localization pairs, transcription, named entities, sentiment, content, and moderation annotation to your taxonomy, in every language above and more on request.
RGB-D captures pairing each video with a synced depth stream, 3D point clouds with per-point range and angles, 3D bounding-box and point-cloud segmentation to your taxonomy.
Motion-signature captures pairing each video with a sensor recording of the same task — including tactile and force-torque streams for contact-rich work.
Everything above, in one delivery. Video, audio, depth, and annotation from the same capture, packed under a single manifest. Raw files never overwritten.
Task demonstrations collected by teleoperation — joint states, end-effector poses, gripper actions, and proprioception, timestamp-synced with multi-view video. LeRobot or HDF5.
On-road and driving footage captured to your brief — forward-facing, cabin, and multi-camera vehicle setups across real traffic, road types, weather, and lighting, with optional timestamp-synced telemetry. Annotated to your schema under one manifest.
G / Generated
Generated from the same consented source material, so synthetic outputs and simulated environments stay grounded in real-world conditions — not stock assets or unconstrained generation.
For rare cases, exact labels, or volumes a camera cannot reach — synthetic data generated from real collections. Labels are deterministic and pixel-accurate, variation is controlled, and no real personal data is used.
Digital twins of real sites — warehouse, kitchen, factory, road — with physics and sensors matched to real captures. Train, stress-test, and regression-test policies before hardware. Scenario scripting to your failure modes, parallel rollouts for reinforcement learning.
03 / Research
Open models for Indian languages and multimodal systems — trained close to the people they serve.
Read the lab notes ↗
Godavari
Dravida Llama
Godavari Vision
TELUGUBENCHMARKSMULTIMODALOPEN WEIGHTSINDIC LANGUAGEVISION04 / JOURNAL
