Edge-first experimental control platform

Tame the chaos of real-world systems

Design sensor-driven systems, run them on the authoritative edge, and let a fleet of edges get smarter from its own operation.

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How it works

Design → Run → Improve

One loop, from the first sensor you sketch to the models your whole fleet relies on.

Design
Compose real and virtual sensor arrays, physics and inference models, and the experiments that exercise them.
Run
Execute experiments and simulations on the edge — the authoritative runtime — in real, simulated, or hybrid mode, deterministically and repeatably.
Improve
Aggregate like-processes across your fleet to refine the physics and inference models that drive your systems.

Capabilities

A time-series lab for sensor-driven systems

Real and simulated experiments, physics and inference modeling, and fleet-level observability — edge-first throughout.

Real & virtual sensor arrays

Build sensor arrays from physical hardware, replay, and model-backed virtual sensors — the same abstraction across all of them.

Deterministic simulation

Fixed-timestep, seedable, replayable simulation for repeatable studies — accelerate time without losing fidelity.

Physics & inference modeling

Bind physics, numerical, and ML/LLM-assisted models into the run loop, with versioning and validation.

Edge-first execution

The edge server owns execution. The cloud observes, aggregates, compares, and suggests — never the real-time control plane.

Fleet oversight

See every edge in one place: which are online, last seen, software version, and the runs they have active.

Cross-run comparison

Group runs that instantiate the same process-class template and compare them across edges for A/B scenario analysis.

The fleet-learning loop

A fleet gets smarter from its own operation

Cloud is the non-authoritative aggregation layer for your fleet. Today it oversees your edges and aggregates like-processes; refining and redistributing models is the next phase. Aggregation is scoped to your own fleet — no cross-tenant sharing.

Oversee
A registry of your fleet: which edges exist, their status, version, and active runs.
Aggregate
Group like-processes across edges into comparable sets — the join key for everything downstream.
Improve
Refine physics and inference models from aggregated like-process data. (Model-loop phase — on the roadmap.)
Distribute
Publish improved templates and models back for edges to pull and adopt — never pushed, never commanded. (Roadmap.)

Start taming the chaos

Design an experiment, run it on the edge, and watch your fleet improve. Marketing site — the platform lives in the app.

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