We build custom multimodal data collection programs that combine vision, audio, motion, depth, device telemetry, language, and task metadata into synchronized datasets for advanced AI and robotics systems.
What We Capture
We turn complex research and product requirements into executable collection workflows, integrating the right sensors, participants, environments, schemas, annotations, and quality controls for your model.
Custom Multimodal Datasets Built for Your Model
Why Choose Our Custom Multimodal Data Service?
End-to-End Solution Design
We translate model requirements into sensor architecture, collection protocols, schemas, labels, acceptance criteria, and delivery workflows.
Flexible Sensor Integration
Our programs can combine standard and custom devices across vision, audio, motion, positioning, force, tactile, and telemetry streams.
Project-Specific Quality Control
We build automated checks and human review around your synchronization, coverage, calibration, privacy, and annotation requirements.
Pilot-to-Production Scale
We validate feasibility with a pilot, then scale participants, devices, sites, environments, and throughput without losing consistency.
Frequently Asked Questions
What is custom multimodal data collection?
It is a collection program designed around your model that captures and synchronizes multiple data types such as vision, audio, motion, telemetry, language, and metadata.
Which modalities can you combine?
We can combine RGB, stereo, depth, thermal, audio, speech, IMU, LiDAR, GNSS, force, tactile, device telemetry, text, and custom sensors.
Can you integrate our proprietary hardware?
Yes. We can work with your devices, APIs, logging tools, timestamps, calibration procedures, and data schema under controlled access.
How do you synchronize different sensors?
We design a unified timing strategy and validate sampling rates, timestamps, drift, dropped data, and cross-modal correspondence.
Can you help define the dataset schema?
Yes. We can design file structure, metadata, identifiers, taxonomies, annotations, validation rules, and versioning with your team.
How do you validate data quality?
We combine automated checks, sensor diagnostics, sample review, annotation QA, acceptance thresholds, and traceable issue reporting.
Can you run a feasibility pilot?
Yes. A pilot is recommended to validate sensors, environments, throughput, synchronization, privacy, and delivery requirements.
What delivery formats are supported?
We support raw and processed files, JSON, CSV, HDF5, Parquet, ROS bags, LeRobot, RLDS, cloud delivery, and custom schemas.