Physical AI & robotics
Egocentric first-person video from skilled trades, with step timestamps, narration, and expert notes.
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Expert human data for AI labs
Real-world expertise, crystallized into training data.
Suralto captures, labels, and verifies data from people who actually do the work — skilled tradespeople, voice talent, and domain experts — so your models perform outside the lab.
6Data categories
6Step consent & QA pipeline
10+Skilled trades covered
5+Delivery formats
ElectricalPlumbingFramingDrywallConcreteHVACRoofingWeldingFinanceHealthcareLegalSoftware engineeringMathematicsPhysicsChemistryMultilingual speechCustomer supportLogisticsElectricalPlumbingFramingDrywallConcreteHVACRoofingWeldingFinanceHealthcareLegalSoftware engineeringMathematicsPhysicsChemistryMultilingual speechCustomer supportLogistics
What we deliver
One partner for every kind of expert data.
From the job site to the benchmark suite: collection, labeling, and evaluation across modalities and domains.
Voice & speech AI
Natural recordings, accurate transcripts, intent and tone labels, and voice-agent evaluations.
Explore →Evaluations & benchmarks
Expert-written eval questions, grading rubrics, and red-teaming that finds real failure modes.
Explore →Expert tasks & RL data
Agent tasks, trajectories, and preference data from practitioners in finance, health, law, code, and STEM.
Explore →Multimodal & vision
Image and video annotation: bounding boxes, segmentation, keypoints, and captioning.
Explore →Custom programs
Something not on the list? We scope bespoke collection and labeling programs around your spec.
Scope a program →Domains
Wherever expertise lives, we capture it.
Physical AISkilled trades
Welding, electrical, plumbing, framing, concrete, HVAC, roofing and more — captured first-person on real sites.
SpeechVoice & speech
Natural and scripted speech across accents and languages, with transcripts, intent and tone.
Physical AIRobotics & automation
Manipulation-relevant footage and labels for robot learning and world models.
RL & evalsCode & agents
Software engineers author tasks, trajectories and rubrics for coding and computer-use agents.
Physical AIField operations
Installations, inspections and repairs — the long tail of how real work gets done.
Expert tasksFinance · Health · Legal · STEM
Credentialed professionals write expert tasks, evals and preference data in their own domain.
How it works
From expert to dataset, in six controlled steps.
A pipeline designed for quality and consent at every stage. Scroll to follow the data.
01
Source
Vetted experts matched to your spec.
02
Capture
Real work, real sites, clear guidelines.
03
Privacy
Faces and PII blurred or redacted.
04
Label
Domain experts annotate and explain.
05
QA
Multi-pass review, senior arbitration.
06
Deliver
Your schema to S3 or Hugging Face.
What you receive
Labels with the context models need.
Every record ships with structured annotations written by practitioners: what was done, how, why — and what an expert would flag that a generalist would miss.
- Time-aligned steps, actions, and labels
- Expert notes and rationale
- Your taxonomy, schema, and format
- Privacy and consent flags on every record
Every delivered recordYour schema
- Source
- Domain, task, modality, expert role & experience
- Content
- Media or text, with timestamps where relevant
- Labels
- Steps, actions, objects, intents, rubrics, scores
- Expert notes
- Rationale, technique, edge cases
- Privacy
- Face blur & PII redaction flags
- Consent
- Contributor agreement & site/owner consent
- QA
- Review passes and approval status
- Format
- JSON · JSONL · CSV · parquet · COCO
Captured where the work happensEgocentric
Speech with intentVoice
Built for robot learningPhysical AI
Quality & trust
Built to hold up to your legal and research review.
Real experts
Contributors are working practitioners in the domain they label — not anonymous crowds.
Documented consent
Contributor rights agreements and site/owner consent, recorded per capture.
Privacy by default
Faces and PII blurred or redacted before data reaches labeling or delivery.
Secure delivery
Access-controlled delivery to your storage, in the formats your pipeline expects.
Start with a sample
See the data before you commit.
Tell us what your model needs to learn. We’ll scope a pilot and send a sample set in your schema.