A hardware-agnostic edge AI reasoning platform. Multi-modal perception, decision fusion, persistent memory, and agentic tool execution — tuned for high inference and reasoning throughput, ultra-low power, and zero cloud dependency.
Running multi-modal inference, sensor fusion, memory, and autonomous decision support at the edge—without cloud, without giving up determinism—still exposes gaps in most architectures.
Raw streams from vision, audio, and sensors must be reduced to structured intelligence on-device. Cloud systems offload this aggregation. At the edge it has to happen locally, in real time, deterministically.
On-device language models run under severe token constraints. Tool schemas, memory, and conversation history consume the budget before the user message is included. The model never sees enough context to reason well.
Cloud agents use large models and unlimited compute to select tools. At the edge you cannot load every schema on every turn. Naive approaches miss the right tool or time out.
Cloud AI offloads memory to external databases. When disconnected or the link is unreliable, there is no external store. Memory must live on-device, survive disconnection, and remain queryable under constrained compute.
Inference alongside hardware control and sensor fusion introduces resource contention. Most edge AI systems avoid this by separating layers. A decision support platform cannot.
Token use is tracked each turn. As the budget tightens, older dialogue is summarized instead of dropped, so the model keeps coherent history within a fixed window—no silent truncation cliff.
A staged pipeline: fast literal matches first, then embedding search over tool descriptions, with an optional model check only when confidence is unclear. Most turns exit early; heavy inference is the exception.
Episodic, semantic, and procedural memory in an on-device vector index—no off-box database. Survives power cycles and loss of connectivity. Retrieval stays within the same compute envelope as inference.
Components exchange typed messages over explicit channels and topics so payloads are known at compile time. Hardware control and inference run on separate paths without shared mutable state, cutting cross-talk and contention.
A deterministic fusion stage ingests model outputs and emits a single structured world-state update. High-rate sensor output collapses into one decision-grade snapshot; layers above the fusion stage consume typed data—not raw frames.
Declarative profiles choose which hardware and agent features exist in a given deployment. Intent policy separates what the model proposes from what the platform may execute—so safety and operations stay explicit, reviewable, and updatable as configuration.
System definitions are data, not compile-time flags: inference, sensing, effectors, behaviors, and model assets are chosen through profiles. Subsystems and the message graph are composed for that profile only—if a capability is absent, it is not constructed and its pipelines are not wired, avoiding dead topics and wasted compute. Retarget the same runtime to different SKUs or environments by swapping configuration instead of rebuilding the stack.
After tool selection, ordered rules evaluate each proposed action against live platform state—interaction mode, rate limits, proximity, quiet windows, and similar signals. Policy can veto execution, answer with text alone, or steer toward an alternate tool when conditions match. Rules ship as versioned configuration, so governance evolves with the product without forking the agent.
Timestamped event log — every perception, decision, and action recorded with context. Survives power cycles.
Structured knowledge graph of environment, entities, and relationships. Continuously updated by the reasoning layer.
Learned behavioral routines encoded from successful past actions. Improves autonomous decision-making over time.
Episodic memory functions as an AI flight recorder — every event, reasoning step, tool call, and decision is logged and retrievable. Full post-mission forensics without cloud dependency.
Security has to hold at every layer—hardware trust, runtime integrity, data at rest, and operational response—not as a bolt-on at the end.