Sano AI delivers failure and recovery training data from live commercial facilities - the data that closes the gap between lab performance and production deployment. Tell us your environment and your failure scenarios. We will collect the data you need.
Whether you are training a foundation model, deploying robots in production, or scaling intelligent automation across your enterprise - Sano AI provides the real-world training data that makes your systems work reliably in the field.
Your policy model needs diverse, high-quality real-world data to generalize beyond controlled lab scenarios. Sano AI provides annotated failure and recovery episodes from live commercial environments - the long-tail data that scales your model's performance on deployment tasks.
Use cases
Your deployed robots encounter failure modes in production that your existing training data never covered. Sano AI captures those exact scenarios - from the live commercial facilities where your robots operate - and delivers them back to your training pipeline.
Use cases
You are deploying or evaluating physical AI robots across your logistics, warehousing, or manufacturing operations. Sano AI gives you a data partnership that directly improves the performance of robots operating in your specific facility environments.
Use cases
We collect failure and recovery data from live commercial environments - the long-tail scenarios that lab teleoperation and simulation cannot replicate. If your robots operate there, we can collect there.
We collect data inside active freight terminals, cross-docks, fulfillment centers, and commercial kitchens through B2B operator relationships. Real operational conditions. Real failure modes. Real recovery behavior.
We specifically capture the data that existing datasets miss - shifted loads, damaged packaging, stack instability, grip failure, and irregular geometry recovery. Each episode is labeled with a proprietary failure taxonomy that maps directly to your model's deployment environment.
Freight and warehouse failure modes require industry-specific annotation expertise built over years of operational experience. Our annotation team understands the difference between a center-of-gravity shift and a packaging defect - and labels them accordingly.
Delivered in LeRobotDataset format with three synchronized camera streams, failure phase labels, and a complete data card. Compatible with pi0, GR00T N1, and all major VLA foundation models. Drop directly into your fine-tuning pipeline.
BIPA-compliant (Illinois), CCPA-compliant (California), individual worker consent for every session, and faces blurred before data leaves the facility. A clean data chain your legal team will approve without friction.
Every engagement starts with a free 20-episode evaluation sample. Your ML team runs it through your fine-tuning pipeline and measures model lift on your own benchmark. There is no purchase commitment until you have seen the results.
The verticals below represent some environment examples and we work with our customers based on their needs. If your robots operate in any facility, factory floor, or commercial setting, we can build a data collection program around it.
Floor-loaded containers and trailers at freight terminals, cross-docks, and LTL facilities. The highest-injury, highest-automation-priority task in logistics.
Mixed-SKU bin picking, tote handling, cart movement, and pallet building in active fulfillment centers and distribution warehouses.
Hot container handling, food service tray manipulation, and equipment movement in restaurant and food production environments under real operational time pressure.
Wet and dry textile handling - hotel linens, restaurant linens, industrial uniforms. Deformable object manipulation in wet states remains one of the hardest open problems in physical AI.
Package sorting, loading sequencing, and manifest handling at delivery depots and sortation facilities - the facility side of last-mile logistics.
Have a deployment context not listed here? Tell us the facility type, the task, and the failure scenarios you need covered. We will build a collection program around it.
Every Sano AI engagement is spec-driven - we deliver in the format your pipeline already uses, on a timeline agreed with your team.
You describe the failure scenarios, environments, and annotation format your training pipeline needs. We build a collection brief around your exact requirements.
We access live commercial facilities through our B2B operator network. Worker consent is obtained, safety protocols are followed, and collection is scheduled around operational hours.
Multi-camera synchronized capture of failure and recovery episodes from live operations. Faces blurred before data leaves the facility. Session logs maintained throughout.
Temporal and spatial annotation using your failure taxonomy. Phase labeling, object state labels, and quality review against inter-annotator agreement thresholds.
Packaged in LeRobotDataset format. Delivered via private Hugging Face Hub repository or S3. Full data card included. Ready for your fine-tuning run on day one.
Every Sano AI dataset is structured to integrate directly into your existing training infrastructure with no conversion step required.
Egocentric (first-person worker view) and multiple exocentric fixed-angle streams, frame-accurately synchronized. Captures both the worker's perspective and the full task context simultaneously.
Temporal segments for: pre-failure context, failure onset, recovery initiation, recovery execution, and task resume. Per-frame bounding boxes with hand state and object state attributes.
Failure types covering: shifted load, damaged packaging, irregular geometry, dropped item, stack instability, and custom types defined in your data specification. Inter-annotator agreement score provided with every dataset.
Collection environment description, worker experience ranges, annotation schema, failure type distribution, known limitations, and IAA metrics. Designed to answer every question your ML team will have before you send the dataset to them.
Individual worker consent forms (BIPA and CCPA compliant), facility data use agreement, and face blurring applied before delivery. Clean for enterprise legal and procurement review.
LeRobotDataset (primary), RLDS, and HDF5. Delivered via private Hugging Face Hub or AWS S3. Compatible natively with pi0, GR00T N1, OpenVLA, and all major VLA training frameworks.
Every engagement starts with a free 20-episode evaluation sample - measure the model lift on your own benchmarks, then decide.
Tell us your deployment environment and the failure scenarios you need covered - we will design a collection program and deliver 20 free annotated episodes. You measure the model lift. Then we talk.
Or reach us directly at hello@trysano.co - no sales pitch, just data.