AI-native mission planning
What we believe

Over the last two decades, billions of dollars went into making single-robot autonomy a reality, from self-driving cars to autonomous drones and humanoids. It is nearly solved. What remains are edge cases that one robot rarely sees but a fleet sees every day. Yet each asset still faces them alone.

We believe the answer is collective intelligence. Reckoni builds a platform that gives a mixed fleet one shared world context, and AI models trained to learn fleet-level policies and run entire missions. Together, they let intelligent behavior emerge at the fleet level, solving problems no single asset could have figured out alone.

Alone, an autonomous robot finishes its task.With Reckoni, the fleet finishes the mission.

Our goal

Build collective intelligence from autonomous fleets and the people who run them.

See it work: pick a mission below, then click the map.

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Step 1 · Set up the incident

or click the map
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Simulation, time compressed. Fleet size is drawn at random for each new incident.

Under the hood

Powered by the Reckoni intelligence platform.

One shared world context, built for efficiency and scale. Frontier large models run in concert with our proprietary RL fine-tuned models, backed by mathematically proven algorithms, so one system can coordinate fleets no team of operators could plan by hand.

Any asset, any vendor
DronesGround robotsVesselsCamerasSensors
Shared world contextOne live picture of the whole operationEvery asset, sensor and track, plus what is still unseen.
Frontier large modelsUnderstand the ask and reason about a messy, open-ended world.
Reckoni RL fine-tuned modelsPlan and coordinate the fleet fast, trained on mission outcomes.
Every plan backed by mathematically proven algorithms:Bayesian searchOptimal task assignmentGuaranteed coverage
One coordinated mission
SearchTrackInspectDetectRe-plan

Shared world context

Every machine connects once and feeds one live picture, so every decision sees the whole operation, not one robot's view.

Models in concert

Frontier models bring open-ended reasoning. Our RL fine-tuned models bring speed and precision, and improve with every mission.

Built for scale

Hardware-neutral and efficient: from a handful of assets to large mixed fleets, re-planning in seconds.

Mathematically proven algorithms

Search, task assignment and coverage use algorithms with proofs of optimality or guaranteed bounds, so plans are provably close to the best possible.

Mixed fleetsMoving targetsPartial informationChanging conditionsThe coordination problems that break hand-drawn plans.
Missions

Built for missions where minutes matter.