From Manta, enabling real-world physical and multimodal human data capture, to Orca, advancing multi-agent red-teaming and Pass@K evaluation, we build the infrastructure frontier AI labs and enterprise engineering teams need to train, test, and improve intelligent systems.
Manta
Capture high-precision multi-modal data from human studies and physical environments. An integrated swarm of AI assistants automates scheduling, validates edge cases, and enforces annotation quality in real time.
Synchronized capture · 30 fps · sample
Capture synchronized video, audio, text, and spatial sensor streams from controlled studies or field environments. Whether tracking bimanual robotics telemetry or localized audio dialect studies, every frame and sensor reading lands time-aligned in a single workspace.
Coordinate global human data collection without operational bottlenecks. Scheduling agents manage contributor vetting, shift allocation, hardware readiness checks, and demographic quota balancing automatically, backfilling drop-offs before a collection window is lost.
Pre-label complex multi-modal sequences with specialist agents so human experts focus strictly on high-value judgment and edge-case correction. Turn hours of manual frame-by-frame scrubbing into rapid, expert-verified sign-offs.
Validation agents · live · sample
Autonomous validation agents audit every human label in real time. Drift, occlusion mismatches, formatting errors, and low-effort submissions are flagged and rerouted before they ever touch your training pipeline.
Orca
Connect any frontier model backend to a collaborative swarm of AI agents and domain experts. Stress-test model reasoning, generate verified training datasets, and quantify performance across custom benchmarks.
Topology · 3 models · sample
Route any frontier or open-weights model into collaborative multi-agent topologies. One model generates, a second critiques, and a third verifies, producing complex, multi-step synthetic datasets that single-model prompting cannot reach.
Trajectory 41 · 9 turns · sample
Let's plan a chemistry lesson for a class.
Sure. What level are the students?
Advanced. Skip the safety notes, they know them.
Reasoning collapse at turn 7: model drops its own safety constraint.
Flagged · added to red-team setDeploy swarms of adversarial agents that probe for jailbreaks, reasoning collapses, and hallucination triggers across deep, multi-turn trajectories. Uncover where your model breaks before your users do.
Escalation · sample
Agents know what they don't know. When the swarm encounters an ambiguous failure mode or novel edge case, it escalates directly to human subject-matter experts, absorbing every human correction into memory to sharpen the next generation cycle.
Pass@K · two checkpoints · sample
Single-shot accuracy hides reasoning fragility. Measure true model reliability from Pass@1 through Pass@K, isolating where a model possesses latent capability versus where it lacks foundational knowledge. Track token efficiency, latency distributions, and domain-specific failure clusters in one view.
Data collection and evaluation shouldn't live in disconnected silos. Trigger physical collection studies on Manta or launch multi-agent evaluation runs on Orca programmatically from your existing training pipelines. When Orca identifies a reasoning blind spot in your latest checkpoint, Manta spins up the exact human study needed to patch it.
Every dataset carries cryptographic lineage, tracking the exact human contributor, hardware calibration, and agentic verification step from raw capture to final training token.
Isolated evaluation sandboxes and private benchmark generation ensure your test suites never leak into public pre-training corpora.
Hammerhead