We design and operate custom collection, annotation, curation and QA pipelines for world models, VLA systems, robotic foundation models and autonomous systems. From egocentric and multi-view human demonstrations to client-provided robot, teleoperation and synthetic data.
5+ years delivering specialized AI data programs. A dedicated physical-AI team, custom annotation tooling, AI-assisted workflows and human QC on every accepted sequence.
We built an internal corpus of industrial and real-world egocentric demonstrations to validate the production workflow end to end. This example shows one sequence moving from raw footage to structured hand, object, action and language output.
The corpus is proof of the pipeline, not a fixed dataset we force into every engagement. Each program is built around the client's model, task family, ontology and quality bar. Example shown for capability demonstration: client programs are designed around their own data, schemas, acceptance criteria and security requirements.
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Most teams do not need a vendor to replace their data operation. They need a specific gap closed. These are the five we are usually brought in for.
Robotics data annotation is not one job. These four layers are delivered separately or together, in one schema, so nothing has to be joined across vendors after the fact.
Frame-level geometry with temporal consistency held across sequences, not just accuracy on individual frames.
What happened, to what, and in what order. Segmented against a task hierarchy rather than flat clip labels.
Language tied to specific frame ranges, phrased consistently so downstream training can join on it.
The layer most corpora skip. Without it there is no way to tell an improving policy from a lucky one.
Human demonstration video supports representation learning, task understanding, action anticipation, planning and human-to-robot transfer. It does not replace robot actions, joint states, force, tactile or control signals, so we work across all four sources rather than arguing for one.
Egocentric, exocentric, fixed-camera and multi-view recordings of real-world tasks, covering industrial and everyday activity. Collection runs to a written protocol with scripted task variations.
Robot trajectory annotation, curation and QA across robot logs and teleoperation sessions. Collection support can be provided where hardware and site access are available, scoped case by case rather than promised in advance.
Scene changes, object persistence, camera movement, physical interactions and action-conditioned sequences for models learning how the world evolves.
Physics-plausibility review, real-world comparison, domain-gap analysis and evaluation-set creation for teams generating data in simulation.
The same six stages run on every program. What changes is the ontology, the source data and the acceptance criteria, all of which are agreed before production starts.
Establish the task family, model objective, data source, output schema and acceptance criteria. If we cannot write down what a correct sequence looks like, we do not start.
Create the collection plan, ontology, annotation guidelines, edge-case rules and gold examples. Annotators are trained against the gold set before they touch production data.
Run a controlled collection, or work with client-owned human, robot, teleoperation, sensor or synthetic data. Long sequences are split into parts so several annotators can work in parallel without losing frame alignment.
Hand-pose, segmentation and tracking models produce a first pass where they materially improve throughput or consistency. Every keypoint and box records whether it is model output or human work, so the two are never confused downstream.
Trained annotators correct the model output, specialist reviewers handle difficult cases, automated checks validate structure and continuity, and manual QC covers every accepted sequence. Reviewers pin flags to a specific segment, frame, object or keypoint rather than returning a whole file.
Deliver versioned output, a QA report and a data card, then refine the pipeline using client feedback and model-performance signals.
Data remains one of the hardest bottlenecks in physical AI. We are not a general labeling vendor that added a robotics page. The tooling, the schema and the review workflow were built for this work specifically.
We have built and tested a workflow that pairs AI-assisted pre-annotation with human review, across keypoints, VLA annotations, object grounding and temporal task labeling, and proved it end to end on our own egocentric corpus.
Our own tooling, not a reseller licence. The interface, schema and review workflow adapt to client-specific ontologies, data types and acceptance criteria.
A dedicated physical-AI team, backed by Biz-Tech's wider specialist network. Trained on the physical and temporal context of the task, not only on how to operate the tool.
Our wider experience spans model evaluation, RLHF, multimodal data and expert-led programs for advanced AI teams, with full NDA compliance and isolated project teams as standard.
India-based with deep industrial relationships, giving a practical route to source underrepresented tasks and environments where site access and consent permit.
What the schema and the review workflow actually enforce.
Different architectures, different task families, same underlying problem: the supervision the model needs does not exist yet.
Teams training models to predict how a scene evolves under action.
General-purpose manipulation and navigation policies that need diverse demonstration data with language grounding.
Fine motor tasks where hand pose, contact and grasp state carry most of the signal.
Picking, packing, sorting and assembly systems needing object interaction and task completion labeling.
Behaviour classification, scenario coverage and long-tail event curation at volume.
Physics-plausibility review, domain-gap analysis and real-world comparison sets.
Academic and industry teams working on embodied AI and sim-to-real transfer, who need structured, well-documented demonstration data with a defined ontology.
Looking at physical AI more broadly, including where we fit across pre-training, post-training and evaluation, or how this sits alongside deployment on a factory floor?
See Physical AI & World Models →We can begin with a narrowly scoped pilot using your existing data or a custom collection brief. Together we define the output schema, quality thresholds and acceptance criteria before production begins.