Jax spatial AI agent suite
Meet the Math Engine
The AI engine that translates natural language into mathematically optimal spatial solutions — across defense, infrastructure, logistics, and beyond.
Mathematical programming is fundamental across domains, yet remains a skill-intensive bottleneck
Mathematical optimization plays a critical role across many business sectors, from supply-chain management to energy systems to logistics planning, where effective decision-making relies on solving highly complex optimization problems.
While practitioners can usually describe these problems in natural language, translating them into precise mathematical formulations that optimization solvers can process remains a skill-intensive bottleneck. Crafting a correct formulation requires precise definition of decision variables, objectives, and constraints — a skill that typically takes years of specialized training in operations research to develop.
Our approach
Math Engine automates this task — translating natural language into executable optimization models, dispatching to the right solver, and self-correcting against domain constraints. No operations research expertise required.
General-purpose AI is good at many things. Math Engine is purpose-built for physical world intelligence.
From defense operations to autonomous engineering, from global threat intelligence to logistics optimization — Math Engine is the flagship engine of a platform that spans nine specialized domains.
Grounded in operations research and spatial intelligence, Math Engine harnesses mathematical programming — producing constraint-verified solutions, following engineering standards reliably, and knowing when to escalate to a human.
In deployment, Math Engine is designed to outperform manual engineering with faster design cycles, lower CapEx, and designed to reduce field rework.
Up to 40% lower infrastructure CapEx*
Designed to reduce capital expenditure through mathematically optimal equipment placement and cable routing
Minutes not weeks
Network designs that take human engineers days to weeks, produced in minutes with full constraint verification
9 domains one platform
Defense, infrastructure, cyber, investigation, simulation, detection, remote sensing, agent systems, and spatial apps
*Target metrics based on architectural design goals. Actual results may vary by deployment.
Fig 1.A — Math Engine
[FIG 1.A.1] Formalize design intent
Natural language design requirements — coverage targets, equipment constraints, budget limits — are parsed into a formal optimization specification: decision variables, constraints, and objective functions that mathematical solvers can process.
[FIG 1.A.2] Encode spatial context
The physical environment — street geometry, building footprints, existing infrastructure, terrain — is structured into a machine-readable spatial representation that the AI reads and reasons over through deterministic queries.
[FIG 1.A.3] Classify problem structure
The formalized specification is mapped to canonical optimization classes — facility location, network flow, vehicle routing, scheduling — ensuring the right solver family handles each subproblem.
0%
[FIG 1.A.4] Generate optimal design
The selected solver produces a mathematically optimal solution — equipment placement, cable routing, resource allocation — as structured, auditable operations on the spatial representation.
[FIG 1.A.5] Verify against constraints
Every generated design element is validated against engineering standards: capacity thresholds, maximum distances, physical laws, regulatory limits, and budget constraints. Violations are detected and repaired automatically.
[FIG 1.A.6] Converge on minimum cost
A mixed-integer programming solver evaluates the design against alternative configurations, converging on the solution that minimizes total deployment cost while satisfying every validated constraint.
[FIG 4.A.1] Formalize design intent
Natural language design requirements — coverage targets, equipment constraints, budget limits — are parsed into a formal optimization specification: decision variables, constraints, and objective functions that mathematical solvers can process.
[FIG 4.A.2] Encode spatial context
The physical environment — street geometry, building footprints, existing infrastructure, terrain — is structured into a machine-readable spatial representation that the AI reads and reasons over through deterministic queries.
[FIG 4.A.3] Classify problem structure
The formalized specification is mapped to canonical optimization classes — facility location, network flow, vehicle routing, scheduling — ensuring the right solver family handles each subproblem.
[FIG 4.A.4] Generate optimal design
The selected solver produces a mathematically optimal solution — equipment placement, cable routing, resource allocation — as structured, auditable operations on the spatial representation.
[FIG 4.A.5] Verify against constraints
Every generated design element is validated against engineering standards: capacity thresholds, maximum distances, physical laws, regulatory limits, and budget constraints. Violations are detected and repaired automatically.
[FIG 4.A.6] Converge on minimum cost
A mixed-integer programming solver evaluates the design against alternative configurations, converging on the solution that minimizes total deployment cost while satisfying every validated constraint.
Introducing the
Jax agent suite
Math Engine
[NEW]The orchestration engine that produces verified spatial solutions. It takes natural language problem descriptions, autonomously formulates precise mathematical programs, and self-corrects against domain constraints. The output is not a suggestion — it is a deployable solution.
Specification
Problem Classification
Analyzes problem descriptions and identifies their mathematical structure — facility location, routing, scheduling, resource allocation, or network flow. This ensures each subproblem is routed to the engine best equipped to handle it.
Specification
Constraint Verification
Translates domain-specific rules into mathematical constraints and validates every generated solution against engineering standards, physical laws, and capacity limits. Detects and repairs violations before any output reaches the operator.
Specification
Solver Execution
Dispatches formulated problems to the optimal solver engine based on problem structure. Multiple solver families cover the full spectrum of spatial optimization — from network design to fleet routing to resource scheduling. Automatic selection ensures each problem gets the right mathematical treatment.
Specification
Built under the leadership
of a world-class spatial AI expert
Building technology of this caliber requires deep expertise in operations research, mathematical optimization, and spatial intelligence — combined with the ability to architect an entire AI agent platform from the ground up.
AI leadership
Ari Aviv
Founder & CEO
Engineering-first approach
to data privacy and security
We have invested heavily in data privacy and security. All infrastructure designs are generated within your project environment.
- Geospatial data is encrypted in transit and at rest
- No customer data is used for model training
- SOC 2 compliance roadmap in progress
- Project-level isolation — your data is never shared across workspaces