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Spatial systems. Grounded decisions.

Intelligence
has shape.

We build spatial AI systems that turn complex information into evidence-grounded, decision-grade intelligence.

Explore the approach
Structure the problem. Make the decision.Independent thinking. Engineered.
01 / The premiseStructure before inference

Better decisions
begin with a
better model.

Before a system can reason about a problem, it needs a way to represent it.

We model entities, relationships, distances, and constraints. Spatial structure makes it possible to ask what is connected, what is nearby, and what is feasible.

That structure gives retrieval context and reasoning boundaries. The result is a decision you can inspect, with evidence you can trace.

Spatial modeling is how we structure the problem.
Decision-grade intelligence is what we produce.

Spatial ≠ only geographic

A space can describe physical locations, semantic similarity, dependencies, or the set of actions a system is allowed to take.

02 / The geometry of a decisionExplore the feasible

Every decision
occupies a space.

Evidence locates the candidates.
Constraints define what is possible.
Reasoning selects a course of action.

A possibility spaceInteractive model
Candidate feasibility under changing constraintsDemand strength is on the horizontal axis. Operational readiness is on the vertical axis. 3 of 8 illustrative candidates meet both thresholds. Candidate E ranks highest among them.01001000DEMAND STRENGTH →OPERATIONAL READINESS →FEASIBLE REGIONABCDEFGH
EligibleSelectedIllustrative data

Change the constraints.
Watch the decision change.

Move either threshold to see which candidates remain possible.

Current decisionCandidate E3 of 8 candidates satisfy both constraints.

Eligible candidates rank by the sum of both scores, weighted equally. This is a teaching model, not a live recommendation.

FIG. 02A simplified view of a decision space. Real systems carry more dimensions.

03 / The systemFrom observation to learning

A chain of reasoning.
An unbroken trail.

Each stage preserves the context needed by the next. Decisions remain connected to their sources, constraints, and observed outcomes.

  1. 01

    Data

    Collect observations.

    Model the problem
  2. 02

    Structure

    Encode relationships.

    Model the problem
  3. 03

    Retrieval

    Find relevant context.

    Ground the reasoning
  4. 04

    Evidence

    Keep the source trail.

    Ground the reasoning
  5. 05

    Reasoning

    Evaluate within constraints.

    Ground the reasoning
  6. 06

    Decision

    Produce a typed result.

    Close the loop
  7. 07

    Outcome

    Observe what happened.

    Close the loop
  8. 08

    Learning

    Update the model.

    Close the loop

Built to be questioned

Show the work.

Retrieval-augmented generation brings relevant source material into the reasoning process. Explicit schemas define the decision output. Policy checks constrain which actions a system may take.

The decision record keeps the evidence, assumptions, and unresolved uncertainty visible.

01Source records
02Retrieved context
03Policy constraints
Decision recordWhat we chose.
Why we chose it.Sources attached ↗
FIG. 03A decision is only as useful as the evidence you can inspect.
04 / An applied explorationResearch in practice

Product decision systems

An opportunity is
a set of relationships.

Illustrative architecture

A promising product is only one part of the problem. Demand, supply, economics, and delivery have to make sense together.

Our product decision research combines retrieval, spatial relationships, and typed decision outputs to make those dependencies explicit.

Product opportunity as a connected problemA product connects to demand, supplier capacity, delivery, and unit economics. These relationships feed a candidate evaluation, which leads to a limited test and observed outcomes.Demand signalsSupplier capacityDelivery constraintsUnit economicsLimited testObserved outcomesEvaluatecandidateLearning updates the model
FIG. 04Illustrative product evaluation. Relationships change the answer.
  1. 01

    What do we know?

    Retrieve product, demand, and supplier evidence with its source and date.

  2. 02

    What can we do?

    Evaluate candidates against delivery, margin, and operational constraints.

  3. 03

    What should happen next?

    Return a structured decision to test, defer, or reject, with the evidence attached.

  4. 04

    What did we learn?

    Compare observed outcomes with the assumptions behind the decision.

05 / Start with the problemEspacial

For problems with more than one dimension.

What shape is
your next decision?

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