Optara Platform
The sensing network,
brain and memory
behind real-world AI.
From a signal captured in the field to context retrieved by an AI agent. Explore how the layers of Optara work together.
System architecture · Our vision
Many vantage points.
One connected understanding.
Observation flow: distributed sensors send visual, audio and environmental signals to Optara Edge, which sends processed observations and selected evidence to Optara OS. Agent flow: AI agents send a scoped request to Optara OS through an API, and Optara OS returns grounded context with source evidence.
Distributed sensing
- Vehicle fleets
- Fixed sensors
- Wearables
- Drones
- Satellites · Planned
- Underwater · Planned
Visual, audio and environmental signals
Optara Edge
Real-time perception · Local processing · Selected evidence
Shared brain and memory
Optara OS
Live world state
Fuse signals across sensors, locations and time.
Deep interpretation
Compute-intensive foundation models connect observations and context.
Persistent memory
Spatial and temporal graphs, embeddings and source evidence.
AI agents & LLMs
Ask through an API. Retrieve physical-world context. Answer in the language of the task.
Processing across the platform
Fast in the field.
Deeper across the network.
At the edge
Interpret local signals in real time. Detect relevant changes close to their source and send processed observations with supporting evidence.
In Optara OS
Combine observations from many devices. Run heavier models, connect related events and build a memory of how conditions evolve.
Agent access
How a question reaches
the physical world.
01 · Request
The agent defines the context
A question becomes a request scoped by place, time, asset or condition.
02 · Retrieval
Optara OS finds relevant evidence
Retrieve observations, relationships and changes from shared memory.
03 · Response
The agent builds a grounded answer
Use the returned context with timestamps, sources and confidence to explain what is known.
Context returned to the agent
- Observed condition
- Location
- Time
- Source evidence
- Confidence
- Change history
Designed workflow. Freshness and coverage remain visible so an agent can distinguish an observation from an inference.
Deployment
Define the mission.
Design the sensing network.
Coverage & connectivity
Choose sensor placement, observation frequency and data transfer around the operating environment.
Interfaces & access
Define which observations and context your teams and agents need, with appropriate access boundaries.
- Current evidence
- TrashTrace vehicle-mounted sensing is in field testing.
- Platform direction
- Broader sensor integration, Optara OS memory and agent access form the development vision. Pilot scope establishes what is available for a specific deployment.
- Planned extensions
- Satellite and underwater sensing.