Ground Truth IntelligenceFor The Dynamic World
The data layer for world models, built by a team of AI scientists and executives
Vision-language-action models, precise manipulation, and online RL are moving fast across the field. Dynamic intelligence is different: we capture and curate the multimodal streams (cameras, depth, force, proprioception), pair them with simulation and regression suites, and wire fleet telemetry so those policies can be fine-tuned, promoted, and audited inside real warehouses and factories—not just on benchmark tables.
The World Models Vision
That ground-truth data layer is the foundation teams need to build world models—learned dynamics of real environments that sit upstream of the policies and simulators you ship.
World models are neural networks that understand the dynamics of the real world, including physics and spatial properties. They can use input data—including text, image, video, and movement—to generate videos that simulate realistic physical environments. Physical AI developers use world models to generate custom synthetic data or downstream AI models for training robots and autonomous vehicles.
Why Robotics Teams Pair Us With Their Model Roadmap
Model labs iterate on architectures and training recipes; operators still need consent-aware logging, labeling contracts, replay for incidents, and gates that block bad weight drops. We build that substrate so your autonomy group spends cycles on learning, not on duct-taped CSV exports.
The Team
Dynamic intelligence is building the data layer for general-purpose AI models powering the moving agents. We are a group of roboticists, engineers, and AI scientists with experience at Harvard and MIT Labs, building the data bedrock for the foundational vision-language-action (VLA) models to support humanoid robots, autonomous vehicles, and physical agents of the future with superior intelligence to thrive in dynamic physical environments.
Why We Started Dynamic Intelligence
Frontier VLAs and manipulation stacks only matter if real robots can train, debug, and ship without mystery regressions. We started Dynamic Intelligence because production fleets already emit the multimodal ground truth—time-aligned sensing, actions, policy checkpoints, and operator context—that research and MLOps actually need. Without disciplined capture, labeling, and verification, even the best model cannot replay a failure, compare behavior across sites, or promote a new checkpoint with confidence when your floor, SKUs, and software change every week.
Data & Verification Products
Shipping to design partners today—designed to sit alongside research policies and OEM stacks:
Ground-Log — time-aligned multimodal trajectories, sparse labels, and failure annotations with retention and access controls for training and eval.
Fleet-Tape — production telemetry, policy version hashes, promotion/rollback hooks, and replay exports for root-cause and compliance.
Bench-Fabric — scenario libraries, domain-randomization jobs, and automated promotion gates before new checkpoints touch live robots.
Dynamic Intelligence Vs. Typical “We’ll Log It Later” Stacks
| Capability | Dynamic Intelligence | Ad-Hoc Logging Scripts | Crowd Labeling Only | OEM Black-Box Cloud |
|---|---|---|---|---|
| Contract-first multimodal capture (RGB-D, force, proprioception) | ✓ | ✕ | ✕ | ✕ |
| VLA / policy fine-tuning datasets with provenance | ✓ | ✓ | ✕ | ✕ |
| Scenario replay and regression before weight promotion | ✓ | ✓ | ✕ | ✕ |
| Human–robot proximity and speed envelopes enforced in data and runtime | ✓ | ✓ | ✕ | ✕ |
| Fleet-wide policy versioning, rollback, and audit traces | ✓ | ✕ | ✕ | ✕ |
| Sim fault injection (latency, slip, glare) tied to real logs | ✓ | ✕ | ✕ | ✕ |
| Safety-case exports for ops, legal, and insurers | ✓ | ✕ | ✕ | ✕ |
| Digital twin wired to live telemetry and historical slices | ✓ | ✕ | ✕ | ✕ |
| Continuous validation when layouts, SKUs, or vendors change | ✓ | ✕ | ✕ | ✕ |