Generative AI Multimodal Data Annotation

High-Quality Sensor Data Annotation for Real-World AI Performance

Train, validate, and deploy perception systems with expertly annotated multimodal sensor data.

Trusted Multimodal Data Annotation for Physical AI

DDD provides end-to-end sensor data annotation services for Generative AI and perception systems that rely on complex, high-volume sensor inputs. We work across LiDAR, radar, RGB cameras, depth sensors, IMU, and sensor data fusion, ensuring every frame, point cloud, and signal is accurately labeled, validated, and enriched for model training and evaluation.

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Our Services

Image Annotation (Detection, Segmentation, Classification)

Label objects and scenes with bounding boxes, polygons, masks, and image-level categories.
Includes clear guidelines, inter-annotator QA, and dataset health reports (coverage, class balance).

Video Annotation (Tracking & Temporal Events)

Annotate objects across frames, track motion, and mark time-based events or activities.
Supports keyframes, frame-by-frame labeling, and exports aligned to common ML formats.

Audio Annotation (Transcription, Speaker & Sound Events)

Create transcripts, speaker labels, and tag acoustic events (e.g., alarms, vehicles, emotions) with timestamps. Delivers clean time-aligned annotations for ASR, diarization, and audio classification models.

Text & Document Annotation (NER, Classification, Relationships)

Label entities, intents, topics, and relationships in multilingual text and digitized documents.
Includes guideline development, adjudication workflows, and gold-standard dataset creation.

Multimodal Alignment (Image–Text, Video–Text, Audio–Text)

Link captions, transcripts, and metadata to the exact regions, frames, or timestamps they describe.
Enables training for VLMs and multimodal search with consistent cross-modal references and QA.

Use Cases We Support

Multimodal Perception Training

Annotate synchronized camera, LiDAR, radar, and IMU data to train robust sensor-fusion models.

3D Point Cloud Annotation

High-precision 3D bounding boxes, semantic segmentation, and instance labeling for LiDAR-based perception.

Edge-Case & Failure Mode Curation

Identify, annotate, and catalog rare, ambiguous, or safety-critical scenarios that challenge model performance.

Autonomous Navigation & Path Planning

Label dynamic objects, drivable space, obstacles, and trajectories across time-series sensor data.

Human-Machine Interaction Modeling

Annotate gestures, pose, intent, and motion patterns for humanoids and collaborative robots.

Medical & Biosensor Data Interpretation

Label time-series and multimodal healthcare sensor data for diagnostics, monitoring, and predictive models.

Agricultural Sensing & Automation

Annotate sensor data for crop health monitoring, yield estimation, and autonomous farming equipment.

Industries We Support

Autonomous Driving

High-precision sensor annotation to improve perception, prediction, and planning in real-world driving conditions.

ADAS

Accurate labeling of safety-critical scenarios to support validation, compliance, and reliability testing.

Robotics

Sensor-rich data annotation for navigation, manipulation, and real-time decision-making systems.

Healthcare

Secure, compliant annotation of medical and biosensor data for AI-driven diagnostics and monitoring.

Agriculture Technology

Sensor data labeling for precision agriculture, autonomous machinery, and crop analytics.

Humanoids

Multimodal annotation to enable perception, motion understanding, and human-robot interaction.

End-to-End Sensor Multimodal Data Annotation Pipeline

Whether you need a one-time dataset or a continuous annotation pipeline, DDD manages the full lifecycle:

Discovery & Scoping

We assess your model goals, sensor modalities, failure points, and performance gaps.

Annotation Strategy Design

Define schemas, taxonomies, edge-case categories, accuracy thresholds, and tooling requirements.

Data Ingestion & Pre-Processing

Secure ingestion, synchronization, and normalization of raw sensor streams.

AI-Assisted Annotation

Combine automation with expert human review to scale efficiently without sacrificing quality.

Domain Expert Validation

Specialized reviewers validate annotations against safety, accuracy, and scenario-specific criteria.

Curation, Enrichment & Metadata Tagging

Cleaned, structured datasets enriched with contextual metadata for reuse and analysis.

Quality Assurance & Audit Trails

Multi-layer QA, inter-annotator agreement checks, and full traceability.

Delivery & Continuous Feedback Loop

Seamless integration into training pipelines with feedback-driven iteration.

What Our Clients Say

DDD helped us uncover and annotate critical edge cases we were missing. Their sensor expertise directly improved our model stability in real-world testing.

— Head of Perception Engineering, Autonomous Vehicle Company

Their ability to handle multimodal sensor data at scale, without sacrificing accuracy, made DDD a true extension of our team.

— Director of AI, Robotics Company

They understood both the technical and compliance challenges of sensor-driven healthcare data. Execution was seamless.

— VP of Engineering, Healthcare AI Firm

From LiDAR to time-series sensor data, DDD delivered clean, consistent datasets that accelerated our deployment timelines.

— CTO, AgTech Platform

Why Choose DDD?

Edge-Case-Focused Approach

We prioritize rare, ambiguous, and safety-critical scenarios that directly impact real-world model performance.

Human-in-the-Loop at Scale

AI-assisted pipelines combined with trained experts deliver high accuracy without sacrificing speed.

Domain-Trained Workforce

Specialized annotators trained for automotive, robotics, healthcare, and industrial sensor data.

Secure, Compliant Operations

Workflows designed to handle sensitive, regulated, and proprietary sensor data with confidence.

DDD’s Commitment to Security & Compliance

Your sensitive sensor data is protected at every stage through rigorous global standards and secure operational infrastructure.

Verified controls across security, confidentiality, and system reliability.

Holistic information security management with continuous audits.

Responsible handling of personal, biometric, and medical sensor data.

Automotive-grade protection for mobility and vehicle AI workflows.

Turn Raw Sensor Streams into Reliable, Model-Ready Intelligence

Talk to an Expert

Frequently Asked Questions

What types of sensor data does DDD support?

DDD annotates a wide range of sensor data, including LiDAR point clouds, radar, RGB and IR cameras, depth sensors, IMU, time-series signals, and fused multimodal datasets.

How do you handle complex 3D LiDAR annotation?

We provide high-precision 3D bounding boxes, semantic segmentation, and instance labeling, validated by domain-trained experts and multi-layer QA processes.

Can you help identify and label rare or safety-critical edge cases?

Absolutely. Edge-case discovery and annotation are core strengths, including rare events, failure modes, ambiguous scenarios, and long-tail conditions.

What industries typically use your sensor annotation services?

Our services support autonomous driving, ADAS, robotics, healthcare, agriculture technology, and humanoid systems, among other sensor-driven AI applications.

Do you support time-series and sequential data annotation?

Yes. We annotate temporal sensor data such as trajectories, motion patterns, signals, and event sequences across frames and sensor streams.

How do you ensure annotation accuracy and consistency at scale?

We use AI-assisted workflows, inter-annotator agreement checks, domain expert validation, and full audit trails to maintain production-grade quality.