VeriPhiLabs turns raw, multi-sensor robot captures into provenance-traced, time-synchronized training datasets — sealed with versioned cryptographic proofs your models can be audited against, frame by frame.
Every dataset we deliver is built on provenance, structured around natural patterns, and sealed with a versioned cryptographic proof.
We embed C2PA v2.3-compliant manifests at the episode level — capturing annotator identity, taxonomy version, QA pass state, and object-level scene trace for every frame in a teleoperation capture.
We ingest ROS2 bag files and synchronize egocentric video, wrist cameras, force/torque sensors, and joint states to sub-frame precision — then annotate across all modalities with a unified episode taxonomy.
Every dataset release is sealed with a cryptographic hash tree across all episodes, annotations, and metadata. When your model fails in production, you can trace the failure to a specific annotation decision — not just a dataset version.
A managed pipeline that handles every stage — so your team stays focused on building robots, not wrangling datasets.
Upload ROS2 bag files via our secure API. We validate format integrity, detect sync drift, and confirm sensor coverage before annotation begins.
All sensor streams are synchronized to sub-frame precision. Object identities are tracked across cameras, and action boundaries are auto-detected as annotation seeds.
Domain-specialist annotators label grasp quality, contact surface, failure type, and action segments — each decision logged with confidence scores and reviewer identity.
Your dataset is delivered with a cryptographic proof manifest, C2PA episode records, and an interactive episode viewer for spot-checking any frame in the collection.
At the heart of multi-sensor perception lies a deceptively hard challenge: aligning data streams that operate at completely different speeds. Cameras often capture 30 frames per second. LiDAR systems may scan at 10 hertz. Inertial sensors produce hundreds of measurements every second.
When these streams aren't carefully aligned, a system can attempt to interpret events that never occurred in the same moment — stitching a camera frame to an IMU reading from milliseconds earlier, or a force spike to the wrong contact event. The result is a distorted view of reality baked directly into the training data.
We don't just label single streams. We analyze relationships between modalities — real and synthetic, across cameras, across conditions — so your models learn the patterns that generalize.
Paired-frame analysis across domains and conditions — quantifying how scene attributes shift while underlying actions stay constant. The foundation for domain-transfer robustness.
Time-synced, time-coded footage from 2–3 cameras, aligned to a shared clock. Object identities are tracked across viewpoints so every annotation is consistent frame-for-frame, camera-to-camera.
3D and 4D scenes generated in Unreal Engine, rendered to match real-capture sensor profiles. Synthetic data amplifies your real episodes — calibrated against ground truth so fidelity stays measurable.
Every dataset version we release is sealed with a Merkle hash tree spanning all 500+ episodes, their individual annotations, and the full provenance metadata. The root hash is your unforgeable audit anchor.
We go deep on domains where data quality is a production-critical constraint, not a research exercise.
Multi-camera teleoperation episodes annotated with contact events, grasp quality, and failure labels — the dataset type that bridges sim-to-real gaps in humanoid hand policies.
High-volume annotation pipelines for autonomous mobile robot operations — object tracking, pick success/fail classification, and spatial episode labeling across shift-length captures.
Complete data governance documentation for high-risk AI systems — C2PA manifests, annotator records, QA evidence, and versioned proof artifacts ready for competent authority review.
Traceable real-world episode data that anchors synthetic augmentation pipelines — the ground truth that calibrates your world model's physics fidelity.
Versioned, provenance-sealed datasets for robotics benchmarks — ensuring that comparative evaluations across labs are run against cryptographically identical data.
When a policy fails in production, trace the failure through your proof manifest to the exact episode, frame, and annotation decision that contributed — without re-collecting data.
We're onboarding a small number of design partners in Q3 2026. If you're building manipulation policies, training foundation models, or preparing for EU AI Act compliance, we want to talk.