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Build Better Vision-Language-Action Models With Better Data
Build Better Vision-Language-Action Models With Better Data
High-quality datasets for vision, language, sensing, and action. Annotated, validated, and production-ready.
When Traditional Vision or Language Datasets Fall Short
- Visual data lacks action grounding or control context.
- LLMs can’t translate intent into physical behavior.
- Models trained in simulation falter in real-world conditions.
- Missing action consequences and trajectory validation.
- Real-world errors lead to costly or unsafe outcomes.
VLA models require multimodal, action-grounded datasets that connect perception, instruction, and outcome.
Digital Divide Data’s Vision-Language-Action (VLA) Solutions
DDD is your end-to-end partner for building Vision-Language-Action (VLA) model datasets, integrating perception, language, and control into unified, action-grounded training pipelines. We combine domain expertise, scalable infrastructure, and rigorous QA to help you move from simulated perception to reliable, real-world action.
Multimodal Data
Collection
Collect and align visual, linguistic, and sensor data, from RGB and LiDAR to simulation traces and instructions, for complete perception-action understanding.
Action-Grounded
Annotation
Label object interactions, trajectories, and task outcomes to connect what models see, are told, and actually do.
Validation and
Closed-Loop QA
Evaluate, review, and refine model behavior through outcome-based validation and multi-pass quality checks.
Governance, Security,
and Scale
Operate with enterprise-grade security, auditability, and compliance to deliver scalable, ethically aligned data pipelines.
Our VLA Workflow
DDD’s VLA process is built to move fast, from defining your needs to delivering production-scale, multimodal datasets.
1. Scope
We define dataset structure, modalities (vision, language, sensor, control), and annotation goals tied to real-world tasks.
2. Pilot
Create domain-specific guidelines, taxonomies, and Through test annotations and validation passes, we link commands, sensory inputs, and actions, refining guidelines for precision and consistency.
3. Scale
Once validated, the pipeline scales seamlessly to production. Our secure global teams deliver scalable, high-quality data with continuous feedback integration.
85%
Acheive more than 85% Multimodal Accuracy
Acheive more than 85% Multimodal Accuracy
80ms
Achieve more than 80ms Latency and responsiveness
Achieve more than 80ms Latency and responsiveness
0.15
Achieve less than 0.15 Disparity Ratio
Achieve less than 0.15 Disparity Ratio
85%
Get more than 85% Satisfaction in Human-AI Collaboration Score
Get more than 85% Satisfaction in Human-AI Collaboration Score
The DDD Difference
Multi-Pass QA
Every dataset undergoes a layered validation process, comprising automated checks, human review, and feedback-based refinement.
Reviewer Training
Annotators are trained on task-specific objectives and simulation environments to ensure consistency and policy alignment.
Policy Outcomes
We evaluate data not just for accuracy, but for its effect on model control, action success, and closed-loop reliability.
Annotation Workflow Expertise
Two decades of experience developing annotation systems for perception, language, and control ensure scalable precision.
Security
We are SOC 2 Type II certified, follow NIST 800-53 standards, and comply with GDPR, ensuring data is protected, private, and handled with enterprise-grade security.
What Our Clients Say
Let’s scope your next VLA pilot and turn perception into action
DDD delivers the multimodal annotation, validation, and governance needed to train, test, and scale embodied AI that performs reliably in the real world.
FAQs
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Vision-Language-Action (VLA) models integrate computer vision, natural language processing, and action reasoning to enable robots to perceive, comprehend, and interact with their surroundings.
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These models allow robots to interpret visual inputs, understand verbal instructions, and execute context-appropriate actions, making them more autonomous and intelligent.
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VLA models power innovations across autonomous driving, industrial automation, assistive robotics, and intelligent home systems.
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While conventional AI focuses on isolated tasks, VLA models combine visual understanding, language interpretation, and action generation into one unified framework, enabling a more human-like interaction with the environment.
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We handle vision, language, and sensor data, including RGB, depth, LiDAR, audio, simulation traces, and telemetry, all synchronized for multimodal alignment.
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Both. DDD supports simulation-to-real workflows, collecting and validating data across simulated environments and physical deployments to improve policy transfer.
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Yes. We design closed-loop validation sets that connect perception, policy, and action outcomes, enabling accurate performance evaluation and retraining.
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Our multi-pass QA and gold-standard reviewer training ensure precision levels exceeding industry benchmarks for multimodal labeling.
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Most pilots are completed within 4–6 weeks, including scoping, sample annotation, QA review, and performance validation before scaling to production.