Deep Ocean Data, Inc. is a technology-driven Embodied AI data engine — not a labor-intensive labeling shop. Our core logic: software-defined data production, algorithm-accelerated closed loops, delivering not just samples but capabilities.
Unlike traditional labor-intensive annotation companies, Deep Ocean Data is built around one core logic: "software-defined data, algorithm-accelerated closed loops." We use automation, simulation, and expert systems to produce data that crowdsourcing cannot touch — and we deliver not just labeled samples, but engineered data capabilities.
We act as our clients' dedicated Data Department — covering everything from sensor strategy and high-difficulty physical capture to expert-grade annotation and full engineering pipeline ownership. Our work determines whether a robot can reason about, interact with, and adapt to the real physical world.
We combine deep integration of MediaLab's frontier AI research with a continuously replenished industry-academia talent pipeline — a technology-intensive model that is genuinely difficult to replicate.
Explore Our Products →"Software-defined data, algorithm-accelerated loops" — automation and AI tooling define our production, not headcount.
Embodied AI training data is at the pre-explosion inflection point. We are building our position now, before the market is contested.
Not samples — capabilities. Data lineage, reproducible eval protocols, and continuously updatable production pipelines.
Our vision is to become the world's most trusted and capable data engine enabling the global AI industry's physical evolution — the essential partner that every serious Embodied AI company relies on to close the gap between digital intelligence and real-world action.
We will redefine the "data engineer" role from low-skill labeler to domain-expert AI collaborator — someone who understands robot kinematics, sensor physics, and task semantics deeply enough to produce data with true instructional value for the machines of tomorrow.
Eliminate the core bottlenecks that block Embodied AI progress: extreme collection difficulty, absent quality standards, and siloed, irreproducible data assets.
Drive the field from delivering "labeled samples" to delivering traceable data lineage, reproducible evaluation protocols, and continuously updatable production pipelines.
Deploy the industry-academia integration advantage: MediaLab's frontier research combined with a scalable, cost-efficient pipeline of expert-trained annotation professionals.
Our product matrix addresses the industry's core pain points across three stages — from high-quality dataset delivery, to an integrated engineering platform, to synthetic data infrastructure that scales across any scenario.
Stage 1 delivers production-grade real-world corpora. Stage 2 turns those corpora into governed, repeatable engineering on E-DEP. Stage 3 multiplies coverage through physics-grounded simulation and synthetic corner cases — feeding improvements back into Stages 1–2.
Full-element datasets for specific scenarios — precision assembly, medical care, indoor logistics — with vision, semantics, action, and tactile data synchronized. They inject physical-world logic and causality into the robot's brain, not mere image classification.
Sub-mm accurate manipulation sequences for assembly, material handling, and collaborative QC — with force-torque and contact-state annotation.
High domain-expert datasets for patient assistance and rehabilitation robots — requiring deep clinical knowledge and rigorous safety validation.
Retail restocking, hospitality, and unstructured domestic HRI — covering the full complexity of human-robot interaction in real-world service settings.
A production system integrating data management, automated annotation tooling, synthetic data generation, and simulation-based evaluation. E-DEP delivers traceable data lineage and continuously updatable pipelines — meeting top-tier clients' demands for true data engineering.
Full provenance tracking, continuously updatable pipelines, and complete annotation audit trails — every dataset is auditable end-to-end.
AI-driven automated annotation integrated with spatiotemporal synchronization — reducing manual labeling cost without sacrificing quality.
Simulation-based evaluation environment enabling reproducible benchmark testing before real-world deployment — closing the training-evaluation loop.
Simulation technology builds a real-and-synthetic training environment, generating corner-case and rare-scenario synthetic data. The core value: dramatically lower the prohibitive cost of real-world collection and address the data scarcity challenge at scale.
1:1 simulation environments with accurate robot kinematics, material physics, lighting, and contact dynamics — deployable for training and evaluation.
Automated generation of rare, dangerous, or difficult-to-capture scenarios — filling data gaps that physical collection can never cost-effectively address.
Continuous feedback between simulation performance and real-world validation — the flywheel that keeps data quality improving automatically.
We embed as your dedicated Data Department — covering strategic consulting through to full execution. Two core service lines cover every engagement model: end-to-end data engineering solutions, and industry-academia training and consulting.
We co-design your optimal perception and capture architecture based on your robot's morphology, operating environment, and downstream training objectives — before a single sensor is purchased.
Teleoperation, motion capture, and Vision-Language-Action synchronization technology acquire the high-quality raw data that no crowdsourcing platform can produce — including dexterous manipulation and failure-mode demonstrations.
A team of robotics, mechanical engineering, and domain specialists — not crowd workers — formulates complex action decomposition rules and arbitrates quality. Every deliverable ships with a full data passport and inter-rater agreement metrics.
Joint university-industry bases equipped with our teleoperation rigs and capture infrastructure. We operate a structured talent development path — Student → Annotator → Annotation Expert — ensuring a continuous supply of professional, domain-trained data engineers.
We help robotics startups and research labs design their own data production and closed-loop iteration systems — from dataset architecture through evaluation framework design and team capability building.
We establish dedicated Embodied AI data production bases — leveraging the MediaLab research network, deep local talent pools, and industry-academia partnerships. These facilities specialize in specific industrial verticals and deliver globally competitive data quality at sustainable operating economics.
Traditional annotation companies are labor-intensive, commoditized, and defenseless against automation. Deep Ocean Data is built on the opposite logic — technology-intensive, algorithmically accelerated, and structurally moated.
Pure manpower, single-task (classification, segmentation).
Handles strongly correlated, temporally synchronized complex multimodal sequences.
Easily displaced by automation; thin and shrinking margins.
Deep university integration — frontier algorithm support and a pipeline of expert-grade talent.
Responsible only for getting the box around the right object.
Delivers reproducible evaluation protocols and a closed-loop iteration engineering system.
Scale headcount to finish large projects — a linear cost model.
Develop automated annotation and synthetic data tools — reducing dependence on pure manpower.
Positioned at the bottom of the value chain — easily substituted.
Defines the next generation of "Data Engineer" — participates in standard-setting at the top of the chain.
Just as human dexterity integrates sight, language, muscle memory, touch, and body-position sense into a single unified action — robot intelligence requires synchronized sequences of Vision, Language, Action, Tactile, and Proprioceptive data captured at matching timestamps with zero drift.
This is the "deep-ocean data" that no crowdsourcing platform can produce. Our teleoperation rigs, motion-capture studios, and expert annotation pipelines exist precisely to capture, label, and validate these high-complexity, high-value streams that sit at the absolute frontier of what robot learning needs.
Our competitive position is built on three structural advantages that compound over time — each one individually significant, together forming a moat that deepens with every project, every partner, and every dataset we produce.
Our structural bond with MediaLab and university partners provides three compounding advantages simultaneously: access to frontier algorithm research before it is published, a continuously replenished pipeline of expert-trained annotation talent, and academic credibility that legitimizes our quality claims in the eyes of enterprise and research clients alike.
We do not just deliver data — we deliver the automated annotation toolchain, simulation-based evaluation infrastructure, and synthetic data generation capabilities that allow clients to extend and maintain their datasets independently. This upstream integration makes us a platform partner, not a transactional vendor, and creates deep switching costs.
We deliver globally benchmark-quality Embodied AI data services at a structurally lower cost base than comparable teams in other major markets. This is not a temporary arbitrage — it is a durable advantage backed by deep talent infrastructure, a strong industry-academia ecosystem, and cost economics that compound over time.
We work shoulder-to-shoulder with China's leading embodied AI teams — from humanoid platforms to industrial arms — on datasets, pipelines, and evaluation that ship to production.
We have substantially finalized a data-processing engagement with a leading robotics company at a tens-of-millions RMB tier — covering high-quality embodied data production, expert annotation, and engineering delivery aligned with their humanoid roadmap.
Together with Lingxin Qiaoshou (Beijing), we are jointly building an embodied data testing platform — integrating benchmark suites, reproducible evaluation protocols, and pipeline hooks so teams can validate datasets and models before production rollout.
Multimodal teleoperation and expert-labeled VLA-aligned sequences for AgiBot (Zhiyuan Robot) — including contact-rich pick-and-place, tool use, and long-horizon tasks with full sensor sync and QA arbitration, under a confirmed tens-of-millions RMB-scale data-processing framework.
Co-development with Lingxin Qiaoshou (Beijing) on an embodied data testing platform: unified benchmarks, regression suites, and E-DEP-aligned workflows so dexterous-hand and manipulation stacks can be stress-tested with traceable, repeatable metrics.
Physics-grounded digital twin and programmatic rare-event generation for a top embodied foundation lab — closing the loop with real-world KPIs so synthetic volume translates to measurable lift on deployment metrics.
Joint program with the Home Economics major: co-building a dedicated track in household-service embodied robotics — scenario libraries, capture labs, and data-training curriculum aligned with real domestic-service robot deployments.
Industry-academia integration with Sichuan Armed Police Officers College on embodied robotics for correctional-facility scenarios and specialist data training — scenario definition, compliance-aware capture, and curriculum for security and custodial robotics applications.
Together with Bingo Tech, we are building BingoClaw — an embodied intelligence stack grounded in the Claw lobster foundation — to power a new embodied product family for home digital assistants and AI companions: perception, dexterous interaction, and closed-loop data that scales from lab to living room.
Partnering with Shenzhen Dajia Catering Management Co., Ltd. to deliver restaurant robotics RaaS built on dexterous manipulation — focused on high-mix, high-flexibility cooking such as sweet soups and tong sui–style workflows. By combining precision operation with a modular intelligent stack, we help chains build an integrated “front-of-house + back-of-house” flexible food factory — unifying dish standardization, lighter staffing, and smarter operations.
Bingo Tech and Lingxin Qiaoshou have brought online in Chengdu what is positioned as China’s first demonstration of green intelligent assembly for designer-toy manufacturing. An in-house, integrated high-precision dexterous-hand robotics line replaces traditional high-pollution manual steps for blind-box figure assembly: no harmful solvents, no human contact on the line, and end-to-end traceable quality control. The project strengthens local cultural IP with advanced manufacturing and supports Chengdu’s push for green, intelligent, high value-added “new quality productive forces.”
We maintain active data-engineering collaborations with many of the country's foremost embodied robotics and humanoid teams.
Partner names are shown for recognition; specific programs and scopes are subject to mutual agreements and NDAs.
Whether you are a robotics company that needs high-quality manipulation training data, a foundation model lab building the next embodied AI breakthrough, or a research institution designing evaluation benchmarks — we are ready to be your end-to-end data engineering partner.