Time‑aware AI infrastructure for agents and devices

Multiscale temporal embeddings, on‑device anomaly detection, and collective timing for robotics, edge AI, and real‑time systems.

Privacy by design. Integer‑native compute. Edge‑ready.

Multiscale time embeddings

Log‑memory tick vectors encode history with constant‑time access.

Built‑in OOD detection

Tier‑wise histograms deliver fast anomaly scores without extra models.

Collective timing

Agents share a compact tick baseline for synchrony and variance reduction.

Curiosity signals

Intrinsic rewards from surprise accelerate sparse‑reward learning.

Anytime compute

Depth‑limited evaluation trades precision for latency on demand.

Edge friendly

Integer‑native kernels and compressed telemetry cut power and bandwidth.

Where it fits

Deployable in embedded stacks and cloud pipelines.

Robotics & AMRs

Stabler control loops and safer exploration via curiosity and OOD.

Autonomous systems

Compact temporal features for perception and planning.

AR/VR & wearables

Compress IMU streams and reduce motion‑to‑photon latency.

Edge monitoring

On‑device anomaly flags with <1 ms scoring budget.

How it works

A comparator ladder converts elapsed time or distance into tiered ticks. Promotions create a logarithmic history. The same ticks feed anomaly scores and optional curiosity rewards.

1 • Encode

Stream events into a multiscale ε–δ ladder to emit a compact tick vector.

2 • Detect

Compare per‑tier histograms to flag OOD and observation‑gap patterns.

3 • Coordinate

Share a group tick baseline across agents to reduce gradient variance.

4 • Act

Use anytime depth to bound latency on edge. Route alerts and rewards.

Request early access

Tell us about your use case. We prioritise robotics, autonomous systems, AR/VR, and edge monitoring pilots.

SDKs for Python, C++, and Rust
Drop‑in operators for popular RL/LLM stacks
Edge builds for Linux + MCU targets