Jax is the AI engine at the heart of Nexma. It understands spatial relationships, engineering constraints, and domain-specific rules — turning natural language into precise infrastructure operations.
98.7%
Spatial accuracy
200+
Domain operations
40+
Languages
Jax combines spatial reasoning, domain expertise, and engineering constraints into a single intelligence layer.
Jax understands topology, proximity, containment, and routing — the fundamental spatial relationships that govern infrastructure.
Schema-driven expertise across FTTH, water, electric, and more. Each domain brings its own rules, standards, and constraints.
Describe what you want in plain language. Jax translates intent into precise spatial operations.
Jax learns from every project — design patterns, field issues, and operational insights feed back into the AI model.
Complex tasks decompose into parallel agent workflows. Research, design, validation, and optimization run simultaneously.
Critical decisions always require human approval. Jax recommends — engineers and operators decide.
Fig 4.A — Nexma AI Engine
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.
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.
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%
The selected solver produces a mathematically optimal solution — equipment placement, cable routing, resource allocation — as structured, auditable operations on the spatial representation.
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.
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.
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.
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.
The formalized specification is mapped to canonical optimization classes — facility location, network flow, vehicle routing, scheduling — ensuring the right solver family handles each subproblem.
The selected solver produces a mathematically optimal solution — equipment placement, cable routing, resource allocation — as structured, auditable operations on the spatial representation.
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.
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.
Jax parses natural language intent and maps it to spatial operations within the active domain schema.
AI generates optimized designs respecting engineering constraints, regulatory requirements, and physical reality.
Every output passes constraint validation — optical budgets, pressure calculations, voltage drop analysis.
Outcomes feed back into the model. Design patterns, field learnings, and operational insights improve future performance.
See how Jax transforms natural language into precise infrastructure operations across any domain.