The closed-loop data-infrastructure platform for Physical AI. Run the whole loop, or plug single components into your existing stack — in our cloud, or in yours.
Nine module groups, one closed loop. Use part or all of the modules, composed your way — adopt the whole loop, or start with the one gate your stack is missing. Wherever it helps, the platform's AI assists: defining the task and scenario, drafting the data plan, pre-labeling, scoring, and writing your next collection instruction.
AI-assisted data-strategy design. Describe your robot, the skill you're chasing, and where it deploys — the platform helps define the task and scenarios, and drafts the data plan the rest of the loop executes.
One catalog across three sources: EgoAtlas premium datasets, a curated open-source index, and your own uploads — with connectors for cloud storage, robot logs and common episode formats. Search, preview, dataset cards, versioning.
Align, connect, sort and dedup multi-sensor episode data — sensor sync, format conversion, versioning and source mapping, so every stream shares one clock and one lineage.
The workbench with automatic annotation purpose-built for embodied data — auto pre-labels refined by experts, with our certified specialist network on tap when you need hands.
The Data Quality Gate: automatic plus human-in-the-loop evaluation and scoring for data quality — every batch scored with evidence before your training run trusts it.
Simulated and synthetic data for augmentation — stretch real episodes across lighting, texture and layout, and generate the edge cases too rare or unsafe to stage for real.
Benchmarks and real-task evals with the Evaluation Gate. Failure mining attributes every miss to its cause and auto-generates the data-strategy instruction that scripts your next capture round — back to the Data Hub, and the loop is closed.
SOTA base models for physical AI — vision-language-action and world models. Browse, fine-tune on your curated data, evaluate, and run.
Fully managed, hybrid, or self-hosted in your VPC. SSO and role-based access. Consent records, bystander handling and an auditable chain of custody — compliance as architecture.
Most tools stop at labels. InfraLoop benchmarks your model on real tasks, mines its failures, attributes each one to a cause, and generates the data-strategy instruction that scripts your next collection round. That's the loop — and it's why every cycle makes your model measurably better.
Log in and run everything in the InfraLoop console — or integrate exactly the modules you need into your own environment. Every module is built API-first.
Sign in and use any or all modules — the fastest path from data to training-ready.
Call any module — quality checks, evaluation, curation — from your own pipelines via REST APIs and a Python SDK.
Deploy individual modules as containers inside your VPC or cluster — a Data Quality Gate in front of your training, on your infrastructure.
Your data stays where it lives. Connectors read it in place — never copied out — and return only results and reports.
Typical patterns: an evaluation gate wired into your training CI · a quality gate on every incoming batch · the full loop, self-hosted. We'll help you pick the right one.