Automatic Dataset QA
Run sensors‑aware checks (drops, skew, coverage, corruption) and generate shareable QA reports per collection.
Audit dataset quality, track model & data lineage, and get regulation‑ready—without slowing engineering velocity.
No spam. Early partners help shape the roadmap.
Robotics teams burn time and money on bad data—only to scramble when audits arrive.
BlackBox makes quality visible, comparable, and provable.
Run sensors‑aware checks (drops, skew, coverage, corruption) and generate shareable QA reports per collection.
Connect datasets, labels, and model artifacts with immutable links—know exactly what trained what.
Capture evidence for audits by design. Export documentation aligned with emerging AI/robotics standards.
We’re partnering with a small number of robotics teams to co‑design BlackBox. You’ll get hands‑on attention and influence the roadmap.
Early feature drops, direct founder support, and a say in what ships next.
Help wiring BlackBox into your existing data stack and ROS bag workflows.
Preferential pricing and credits applied to your first production contract.
Focused on tangible wins for dataset QA and auditability.
Our team has shipped production robotics and ML systems. BlackBox is the tool we wished we had to trust datasets and pass audits without drama.
QA isn’t a checkbox. When you measure quality continuously, compliance docs become a by‑product—not a fire drill.
We’re hands‑on with a few partners to nail the core workflows before scaling access.
Join the waitlist to shape BlackBox from the ground up.
No pitch spam. Occasional product updates only.
We’ll reach out when your org is a good fit for the pilot.