The engine behind Jax
Meet the Math Engine
The engine that translates natural language into mathematically optimal spatial solutions — and commits nothing it has not verified against the real-world constraints of each domain.
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 describes infrastructure fluently, yet returns designs that fail on inspection. Math Engine is purpose-built to produce designs that are verified buildable.
It separates two concerns that are usually conflated. Generality comes from an ontology — a typed model of a domain's entities, relationships, and constraints. The same agent engineers fiber, water, power, or logistics by reading the schema, not by being rebuilt for each.
Feasibility comes from verifiable optimization. Every decision is formulated as a classical optimization problem, solved deterministically, and validated against the domain's physical constraints — nothing is committed until it has passed.
The result is a plan that is buildable, not merely plausible — produced in a single session, and checkable line by line against the standards each domain demands.
Lower build cost
Designs minimize total installed cost — equipment, cable, and civil works — by solving for the cheapest arrangement that violates no constraint.
Faster design cycles
Network designs that take human engineers days to weeks are produced in a single session, with full constraint verification.
Many domains, one agent
Fiber, water, power, wireless, logistics — the same agent engineers each by reading its ontology. Change the schema, not the code.
Architectural design goals, not benchmarked results. Measured solver runs across domains are reported in the research paper below.
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.
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[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.
One agent,
one verifiable loop
Math Engine is not a suite of separate models. It is a single agent running one loop for every decision it commits — recognize the problem, formulate it, solve it, then validate and self-correct before anything is trusted.
Recognize
From the intent and the domain's ontology, the agent identifies which class of optimization the goal implies — facility location, routing, scheduling, network flow, or steady-state simulation. The same recognition runs for every domain; what changes is the ontology it reads, not the agent.
Specification
Formulate
The agent constructs a concrete optimization instance — decision variables, objective, and constraints — populated entirely from the ontology and expressed in a uniform problem contract. Class-specific guidance steers it away from the formulation errors characteristic of each family.
Specification
Solve
The instance is dispatched to a deterministic solver that returns a feasible — and, where an objective exists, optimal — result, with its optimality gap. A small family of classical solvers covers the full spectrum, and engine selection is automatic from problem structure.
Specification
Validate & self-correct
Every solved result is checked against the domain's full constraint set — including physical checks the formulation may not encode. Nothing is committed until it passes; a failed check returns named diagnostics that drive a bounded re-formulation. The design that ships is the first one that passed, not the first one produced.
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