Overview
Data infrastructure for teams teaching machines to operate in the real world.
MDL Technology builds data programs for embodied AI, VLA and world-model development. We bring together real-world collection, simulation, game-world data and structured 3D assets so training teams can move from an identified capability gap to usable training material.
01 / Company background
Built for the operational reality of AI data.
MDL Technology has developed data operations since 2017. Our work sits between data source and model development: we translate a customer's target capability into tasks, environments, sensor requirements, data structure and a delivery plan that can be repeated at scale.
We serve more than 20 leading enterprises across global technology and industrial markets. Our role is not simply to supply volume. It is to provide the structured, quality-controlled material that makes a data program useful to a research or product team.
02 / Team & operations
Specialist teams across the full data workflow.
Our collection and annotation teams execute task-driven programs from sensor setup through data cleaning, labeling and acceptance. Game and simulation teams extend that work with structured visual worlds, controllable variation and engine-level outputs. Together, these groups allow a program to combine real-world evidence with scalable synthetic coverage instead of treating each modality in isolation.
03 / Data capabilities
Coverage across the physical intelligence stack.
Ego & RGB-D data
First-person task demonstrations can be designed around defined environments and task libraries, with synchronized visual, depth, pose, language, action or task-level signals as required by the program.
Simulation data
Controllable UE5-grade scenes support task, object, lighting, camera and behavior variation. Structured outputs can include RGB, depth, segmentation, surface normals, optical flow, camera parameters and task metadata.
3D & game-world data
Our library includes more than 20 million cleaned and structured 3D assets, alongside CAD, PBR, motion, game behavior and G-buffer data for pretraining, reconstruction, world-model and evaluation work.
04 / Real-world coverage
Task environments designed around how people actually work and live.
Our real-world programs span more than ten environment categories and over 200 granular scenarios. Coverage includes retail and supermarket operations, logistics and warehouse workflows, care and senior living environments, pharmaceutical operations, hospitality, residential settings, light industrial work, food service, cleaning and maintenance, office and shared-space routines, education and public-service environments.
Within each category, a program can vary task flow, lighting, time of day, operator, viewpoint, object state, interruption, exception and long-tail behavior. The goal is not to create a generic footage library, but to build coverage around the conditions a target model must recognize and act within.
05 / Quality & delivery
A layered quality system, agreed before collection begins.
We organize quality assurance across raw capture quality, task validity, annotation consistency, delivery usability and privacy compliance. Before a project starts, MDL and the customer can jointly confirm device parameters, environment scope, task lists, annotation fields, data formats, sampling ratios and acceptance thresholds in a project-level Data Quality Delivery Specification.
Raw capture quality
Files are validated for readability, agreed resolution, frame rate, codec, duration, naming and sensor integrity. Samples with missing frames, severe focus or exposure issues, extended occlusion, device faults or empty footage are identified before delivery.
Task validity
We check that the task is complete, key interactions and results are visible, and the recorded flow matches the agreed task script or scenario requirements. Irrelevant, repeated or indeterminate samples are removed or reworked.
Annotation consistency
Scene, task, object, action, task phase, key event and outcome fields are checked for completeness and consistent interpretation. Visual, language, motion, depth, pose and event signals are synchronized where the program requires them.
Delivery usability
Every delivery is reviewed against the agreed schema, packaging, versioning and acceptance thresholds so it can move directly into a customer training or evaluation workflow.
Privacy & traceability
Collection authorization, privacy handling, de-identification requirements, data provenance, revisions and delivery manifests are tracked at the project level.
How quality is measured
Acceptance criteria are tailored to the model objective, sensor configuration and data type. Common dimensions include data completeness, file and sensor-stream integrity, visual usability, valid sample rate, duplicate rate, task completion and outcome visibility, annotation accuracy and consistency, scenario diversity, and time or coordinate alignment across multimodal signals.
Automated checks
File readability, format, resolution, frame rate, duration, timestamps, naming, field completeness, duplicate rate and baseline image quality are checked across the full delivery.
Stratified review
Human review is sampled across environments, task types, batches, operators and difficulty levels to validate task logic, action visibility, annotation accuracy and practical training value.
Priority verification
High-value tasks, exception cases, customer-priority fields and complex samples receive increased review coverage. Full manual review can be used where the program calls for it.
Closed-loop acceptance
Issues are located, removed, reworked, recollected or replaced. Each delivery can include a quality report, sampling record, issue log, revision history and version notes for customer review.
06 / Partnership model
Begin with a focused data program. Scale what proves useful.
Most engagements begin with a scoped pilot: align on the target capability, define a data schema and quality threshold, then review the delivered sample with the customer's technical team. Once the data fit is confirmed, we can scale through recurring collection, simulation generation, asset delivery or a combined multimodal program.