Long tail
Rare edge cases cause most failures.
Robots learn from first-person demonstration. Thoth collects egocentric data at scale while building the intelligence layer that guides, improves and validates every capture.
The Problem
Reality is the missing dataset.
Today's robotic systems are trained on carefully curated datasets, yet deployed into environments that change every second.
Distribution shift
The real world moves. The dataset doesn't.
Unseen scenarios
Deployment meets what training never saw.
The Solution
We built the collection system
so waste never happens.
Most training data gets thrown away. Untrained gig collectors move too fast, drift out of frame, and ship footage a model can't learn from.
The Principles
Trained people
Career collectors
Each collector completes a structured onboarding and certification pass before any paid work begins.
AR-guided capture
Live overlays on spec
A heads-up overlay tracks framing, distance and lighting in real time, flagging a retake before a bad clip ever leaves the rig.
Measured, not guessed
Gauged against reference
LiDAR and stereo cameras scan the full capture volume ahead of each session, so every trajectory is measured against real geometry, not estimated.
Annotated at source
Labels applied mid-session
Pose, contact and object labels are generated alongside the raw footage, so nothing waits in a separate annotation queue before it's usable.
The Scan
Before we record,
we scan.
Calibrate
Three LiDAR heads register the rig volume.
Scan
A full point cloud before contact.
Verify
Both rigs matched to the reference scan.
Start small, scale fast
10Hour sampleAvailable today
200Hours deliveredTailored data
CustomMonthly volumeContinuous supply
Getting Started
Start by looking at real data, not a deck.
70+
Collectors
Career collectors on our payroll across every site — not gig workers hired per job.
1:5
QA ratio
One dedicated QA reviewer for every five collectors, checking each session before it ships.
<1%
Drop rate
Fewer than one percent of recorded clips are discarded for quality — the rest is usable data.
Zero
Outsourcing
Every session is captured in-house. No third-party vendor ever touches the pipeline.
30fps
Capture rate
Every trajectory recorded at a full 30 frames per second, so fast motion stays legible.
20+
Sites
Active capture sites across regions, so the data reflects real-world variety, not one room.
The Workflow
The intelligence layer we're building.
We're building the loop where every deployment decision generates higher-quality data — reducing annotation cost while improving accuracy.
Plenty of companies can collect first-person video. Thoth builds the intelligence layer that decides what should be collected, what should be labeled, and how every dataset becomes more valuable over time.
- Active Learning
- Automated Quality Control
Active Learning
We're building the layer that detects uncertainty, prioritizes missing edge cases, and decides what to collect next.
Automated Quality Control
We're building automated validation that checks every recording and annotation for consistency and accuracy before it enters the learning pipeline.
The horizon: your model tells us what to collect next — so every added hour beats a random hour.
01
02
03
04Our Mission
Our Mission
“Collect the data that lets models learn from real-world experience — reducing annotation cost and improving with every interaction.”
Pedro Alves
(CTO, Thoth AI)
Get started
Building a robot? Let's talk about the data it actually needs.
Talk to our team
A 30-minute call with the team who runs robotics data capture. No sales deck.
Thoth AI — Embodied AI & Robotics
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