MULTIMODAL ANNOTATION SERVICES
Bring Different Data Sources Into One Clear Picture
Camera, LiDAR, radar, and audio each capture a different part of the real world. Anotag helps AI teams annotate and connect these data sources, creating structured datasets that bring objects, actions, and events into context.
THE CHALLENGE
The Difficult Part Is Connecting the Data
Collecting data from multiple sensors is only the first step. Each source captures a scene differently, so annotations must connect the right objects and events.
A pedestrian may appear clearly in a camera frame but only as scattered points in LiDAR, while audio may capture an event shown in the video. Clear annotation rules help establish these connections and handle uncertain cases.
Object Matching
Identify the same person, vehicle, or object across sensors using consistent labels and reliable visual or spatial evidence.
Time and Context
Align video frames, audio, and sensor readings using timestamps. Missing or inconsistent timing can limit reliable matching.
One Set of Rules
Use consistent labels, attributes, event boundaries, and annotation guidelines across annotators and data sources.
ANNOTATION CAPABILITIES
Annotation Across Multiple Data Types
Anotag annotates individual data sources and connects related objects, actions, and events across modalities. Each capability addresses a specific part of the annotation process, from visual labels to cross-sensor relationships.
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Image Annotation
Label objects, people, and regions in still images using bounding boxes, polygons, segmentation masks, keypoints, and image-level classifications for visual AI tasks.
Video and Object Tracking
Track objects across video frames, maintain consistent identities, and label actions and events to capture movement and scene changes throughout a video sequence.
LiDAR and 3D Point Cloud Annotation
Label 3D point clouds using bounding boxes, point-level segmentation, object classes, and attributes, with camera associations where supporting sensor data is available.
Radar Annotation
Label radar detections, object tracks, and movement attributes using defined categories, connecting observations with other sensor data when reliable associations are possible.
Audio and Speech Annotation
Transcribe speech, identify speakers, label sound events, and assign timestamps, linking audio segments to corresponding actions or events in synchronized video.
Text and Metadata Annotation
Label transcripts, descriptions, event records, and sensor metadata with defined categories and attributes, connecting text to relevant visual or audio information.​​
Cross Modal Object Association
Match corresponding objects across camera images, video, LiDAR, and other supported sources using available timestamps, spatial information, calibration data, and project rules.
Temporal and Event Alignment
Label event boundaries, frame ranges, audio segments, and timestamps, connecting related observations while flagging incomplete timing information or uncertain event relationships.
Custom Multimodal Annotation
Create task specific annotation schemas for visual, spatial, acoustic, textual, and sensor data, defining labels, relationships, attributes, review rules, and output formats.
TECHNICAL DEPTH
Choose the Labels That Match the Task
The annotation method should follow what the AI system needs to recognize or understand. A project focused on object detection needs different labels from one focused on 3D perception, speech, or event understanding.
Keep every project's pipelines reliable, accurate, and fast to deliver, and keep improving how we build them.
MULTIMODAL ANNOTATION PROCESS
From Raw Data to a Dataset Ready for Use
The work begins by understanding the data and the intended task. From there, each stage prepares the dataset for the next, with issues recorded and checked rather than silently carried forward.
AI + HUMAN ANNOTATION
Use Automation Where It Helps. Keep People in the Loop Where It Matters.
Automated tools can suggest labels and handle repetitive tasks, but they can also miss unusual objects, confuse similar events, or make incorrect associations between sensors. Human review remains important when the evidence needs interpretation.
AI Pre-Annotation
Let tools draft first-pass labels like boxes, segments, or speech segments for annotators to review.
Human Annotation
Annotators apply the rules, settle hard cases, and use real-world context where a model's prediction isn't enough.
Cross-Modal Review
Confirm linked observations truly match. A camera vehicle is not automatically the nearest LiDAR cluster.
Feedback Loop
Log recurring errors and disagreements. Approved clarifications update the guidelines for everyone after.
QUALITY & VALIDATION
A Label Is Only Useful If It Is Consistent and Correct
For multimodal datasets, checking each file separately is not enough. A correctly drawn box can still be linked to the wrong 3D object. Quality assurance must cover the labels and the relationships between them.
Label and boundary checks
Check objects, regions, classes, and attributes against the project guidelines and acceptance criteria.
Object identity checks
Catch inconsistent IDs, broken tracks, duplicate identities, and wrong associations across frames or sensor views.
Timing and alignment checks
Validate timestamps, frame references, event intervals, and cross-modal links against the source information.
Schema and completeness checks
Confirm required fields, identifiers, labels, and relationships are present and follow the output structure.
Human review of difficult cases
Route ambiguous, conflicting, or low-confidence cases for review. Record decisions that affect similar examples elsewhere.
Final delivery validation
Check the final deliverable against quality thresholds and the output spec. Document any limitations you find.
DOMAIN & SCALE
Multimodal Data for Different Real-World Applications
The same annotation approach does not suit every industry. A robotics dataset may prioritize spatial relationships and object movement, while a speech-and-video dataset may focus on speaker identity, timestamps, and event context.
WHY ANOTAG
The Details Matter When Data Has to Work Together
Multimodal annotation brings several kinds of work into one dataset. The important part is making sure the labels remain useful when the data moves from annotation into model development.
Task-First Workflow
Start with what your AI needs to recognize, then define modalities, labels, relationships, and delivery needs.
Shared Rules
Use one set of definitions for classes, attributes, timestamps, and IDs across every data source and stream.
Linked Review
Check the links between annotations, not just labels within each source. Catches mismatched objects and broken tracks.
Ambiguity Handling
Define how annotators treat occlusion, missing data, uncertain IDs, and conflicts, with a clear review path.
Structured Delivery
Agree on the output schema and metadata up front so annotations are ready for the next stage.
SECURITY & DELIVERY
Clear Data Handling. Defined Deliverables.
Multimodal datasets can include video, voice recordings, location information, and proprietary sensor data. The handling requirements need to be agreed before work begins.
Data Access and Confidentiality
Define who can access source files and annotations, along with any confidentiality or contractual terms that apply to the project.
File Handling and Retention
Agree how data is received, stored, transferred, and removed at engagement end. Document retention and deletion before production starts.
Annotation Traceability
Maintain versions of the schema and guidelines, record review decisions, and track corrections so changes stay clear and understandable.
Defined Delivery Formats
Specify file formats, field names, identifiers, timestamps, metadata, and documentation before annotation begins, so nothing is ambiguous later.
Final Handover
Deliver the annotated dataset against the agreed scope and acceptance criteria, including documented exceptions or unresolved source-data limitations.
WHO THIS IS FOR
Multimodal Annotation for Real World AI Applications
Anotag supports teams working with multiple data sources, from camera footage and sensor readings to audio, text, and 3D data. We build annotation workflows around the information each application needs to recognize, connect, and understand.
AI and Machine Learning Teams
Prepare labelled datasets that connect images, video, audio, text, and sensor data for multimodal model training and evaluation.
Robotics and Autonomous Systems
Annotate camera feeds, LiDAR point clouds, radar detections, object tracks, and spatial relationships for perception systems.
Healthcare and Life Sciences
Connect medical images, clinical recordings, transcripts, and related metadata for defined healthcare AI and research applications.
Manufacturing and Industrial AI
Label inspection images, production video, machine sounds, and sensor events to support quality inspection and industrial AI workflows.
Media and Conversational AI
Align video, speech, transcripts, speaker identities, and sound events for systems that interpret conversations and audiovisual content.
Geospatial and Environmental Teams
Connect aerial imagery, satellite data, and field observations for accurate mapping, environmental analysis, and ongoing monitoring.
Security and Video Analytics
Annotate surveillance footage, audio events, object movements, and timestamps for video intelligence and scene understanding applications.
Enterprise AI and Product Teams
Build structured multimodal datasets with consistent labels, linked entities, and agreed output formats for existing AI products and data pipelines.
FAQ
Questions About Multimodal Annotation
Learn how multimodal annotation connects images, video, LiDAR, radar, audio, and text to create structured datasets for AI training and evaluation.
Turn Your Multimodal Data Into AI Ready Datasets
From individual data sources to connected objects and events, Anotag helps you define the right annotation approach for your AI application, with clear guidelines, quality checks, and structured outputs.
Tell us about your data, your AI goals, and your project requirements.
👉 No commitment. Quick walkthrough.