Native Multimodal Data Engine

Better data.
Capable agents.

Real interaction traces, long-form video and structured multimodal data give agents verifiable capabilities in complex business environments.

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Core capabilities

GUI traces
Visual grounding
Video replay
Action labels
Scene structure
Model evaluation

Data across every modality

Our data services

Purpose-built services for each data modality, brought together in one consistent enterprise delivery process.

01 / TRACE

GUI trajectory capture

Capture every click, keystroke and focus change in native environments. Reconstruct the context of real workflows across desktop apps, browsers and enterprise systems.

  • Synchronize screenshots, keyframes, screen recordings and event streams.
  • Built for agent training, framework evaluation and RPA workflows.
02 / EMBODIED

Embodied AI data

Collect trajectories in real or simulated environments for robotics and embodied agents. Detailed perception, decision and action annotations help models learn objects, spatial constraints and task completion criteria.

  • Capture manipulation, navigation and scene interaction, with video, screenshots, keyframes and trajectories.
  • Annotate action stages, object states, pick-and-place locations, pose changes and failure causes.
03 / TEMPORAL

Temporal action data

Time-aligned screen-recording segments and action sequences help models learn what to do first, and what comes next.

04 / AUDIO

Speech & audio

Customer-service audio, command-word extraction, long-form conversation transcription, and accuracy evaluation for TTS and ASR models.

05 / NLP

Domain text data

Turn specialist documents and complex knowledge bases into structured text, question-answer pairs and instruction datasets.

06 / PRIVATE

Private delivery

Enterprise delivery with programmatic QA sampling, data cleansing and de-identification, aligned with your security requirements.

Built-in quality assurance

Engineered for quality

Turn complex interaction logs into consistent, structured datasets with cross-platform parsing and traceable quality checks.

Double-blind QA

Automated sampling, cross-level checks and double-blind review are built into the pipeline to verify ground truth beyond a single manual inspection.

Unified annotation

Desktop, browser and mobile data share one annotation structure. Page trees, action traces and temporal frames use consistent coordinates and logic for coherent delivery.

From specification to delivery

A reliable delivery flow

Four stages keep every delivery traceable, from agreeing on the schema to packaging the final dataset.

01

Define the schema

Align on the use case, temporal objectives, acceptance criteria and internal data requirements before collection begins.

02

Set up collection

Define capture points, task breakdowns and parameter standards, then configure the parallel batch collection workflow.

03

Run & review

Run collection at scale, combining automated checks with expert review to identify and correct data quality issues.

04

Package & deliver

Deliver cleaned, structured data packages alongside detailed error analysis and evaluation reports, ready for your data pipeline.

Meet the team behind the data

Learn about our company and team, or tell us about your project to start a conversation.

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