Data infrastructure for Physical AI

Before AI can act, it must understand the physical world.

We build high-quality real-world datasets that power robotics foundation models, world models, and Physical AI. From human demonstrations and egocentric video to multimodal sensor data, we bridge the gap between physical experience and intelligent machines.

1M+
frames delivered
40+
task categories
7+
sensor modalities
$0
before quality passes

Every intelligent robot starts with experience.

Human demonstrations are one of the most scalable sources of real-world training data. Neotriclabs captures these demonstrations today, while expanding toward additional data modalities for Physical AI.

Available today

● Live

Real people performing real-world tasks.

Egocentric capture

Future data modalities

TeleoperationComing soon
Multi-camera captureComing soon
Robot fleet dataComing soon
Synthetic dataComing soon
World models related data Coming soon

Our data collection pipeline

Every dataset moves through a repeatable pipeline — planning, capture, quality review, annotation, validation, packaging, delivery — so reliability is built in, not bolted on.

Phase 01🗂️

Plan & capture

Scoped to your spec. Human demonstrations and egocentric video across GoPro, glasses, phones, depth cameras, IMU, and audio.

Phase 02🔬

Annotate & validate

Actions, contact events, and objects — labeled, double-reviewed, and validated against the spec. Edge cases included, not dropped.

Phase 03📦

Package & deliver

Versioned, documented, temporally synced datasets — packaged production-ready and delivered straight into your pipeline.

"We don't just collect data. We engineer reliable data pipelines that transform real-world interactions into production-ready datasets for Physical AI."

Production-grade data quality

Infrastructure companies sell reliability. Every dataset meets a fixed spec before shipping. Metrics don't pass? We reshoot. This is the bar we hold.

Resolution

4K · 60fps

Sharp enough to read fine contact and micro-adjustments frame-by-frame.

minimum
Usable frames

≥ 97%

Blur, glare, occlusion get cut. We don't pad the numbers.

threshold
Annotation accuracy

≥ 98.5%

Actions, contact events, objects — all double-reviewed by humans.

verified
Temporal sync

≤ 120ms

Video, gaze data, IMU motion — all aligned to one clock.

measured
Coverage

40+ tasks

Across people, lighting, and environments — edge cases included. No monoculture.

guaranteed
Versioning & docs

100%

Every dataset versioned and documented. Signed consent, PII scrubbed on request.

non-negotiable
Our commitment

Built to spec. Delivered to production.

Data contracts are full of surprises. We put the risk on our side. Agree on the spec, get a free pilot, and pay only after we meet the bar. If we miss, we reshoot on us.

01 / Spec

Agree the contract

What "good" means for your model — tasks, annotations, metrics. In writing.

02 / Pilot

Free sample batch

No cost. Test it against your pipeline, your loss function, your real use case.

03 / Verify

Quality gate

Every metric measured against the spec. Anything that misses: we reshoot and re-verify.

04 / Invoice

Pay on acceptance

Only after you sign off. Quality first. Payment after.

Start a free pilot →

Physical AI is entering its foundation-model era.

But every breakthrough depends on better real-world data. Most organizations spend months collecting, cleaning, validating, and organizing datasets before training can even begin.

Neotriclabs is building the data infrastructure that makes this scalable — human demonstrations, real-world capture, dataset engineering, and the quality systems that turn raw interaction into production-ready training data.

We're a small team out of Hyderabad. We build and ship. Quality is non-negotiable. Reliability comes first.

Building Physical AI? Let's build your data pipeline.

Tell us your data requirements. We'll design a production-ready data pipeline tailored to your models.

Web
neotriclabs.in
Location
Hyderabad, India
Phone
+91 9177526061
For
Robotics teams · ML engineers · Physical AI & world-model research