NEXMA
The AI Workspace for Spatial Intelligence
JAX
Your unfair AI advantage.
One AI agent for the entire physical world. The physical world runs on millions of decisions — where to build, what to inspect, how to optimize, how to operate. Today, every one of them requires manual work, specialized tools, and human experts who are expensive and scarce. Jax, our Spatial General Intelligence, connects directly to your data, reasons about it spatially, and acts autonomously while keeping a human in the loop.
One platform. Every data layer. Every tool. One AI.
Nexma handles the most demanding infrastructure deployments through a continuous optimization loop. Define your project parameters, train the AI on your standards and constraints, validate designs before breaking ground, deploy across every phase — then analyze and improve with real-time insights.
DISCOVER
AI-powered geospatial analysis identifies optimal network topology from satellite imagery and street-level data.
DESIGN
Mathematical optimization engine generates field-ready FTTH plans validated against industry standards.
DEVELOP
Splice schedules, optical budgets, and bill of quantities — production-ready documentation in minutes.
DEPLOY
Autonomous crew dispatch and real-time field validation ensure first-time-right installations.
Fig 4.A — Nexma AI Engine
[FIG 4.A.1] Formalize spatial intent
Design intent — addresses, coverage targets, architecture constraints — is parsed into a formal spatial specification: a structured, human-readable encoding of every node, edge, and constraint the model will reason over.
[FIG 4.A.2] Query the spatial representation
The engine reads directly from a structured representation of the physical environment — street geometry, building footprints, existing infrastructure, and regulatory boundaries — through deterministic path-based queries, not probabilistic search.
[FIG 4.A.3] Prioritize by engineering relevance
Retrieved spatial elements are scored against engineering criteria — route feasibility, infrastructure reuse potential, capacity headroom — filtering the representation to only the highest-relevance inputs for generation.
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[FIG 4.A.4] Write to the spatial structure
The model produces design decisions by writing directly into the spatial representation — placing equipment, defining routes, assigning allocations — as structured, auditable operations on the network's formal topology.
[FIG 4.A.5] Verify against physical constraints
Every generated operation is cross-checked against hard physical limits: capacity thresholds, maximum distances, cascade depth, pressure ratings, voltage limits, and compliance standards. Violations are rejected before they propagate.
[FIG 4.A.6] Converge on minimum cost
A mixed-integer programming solver evaluates the generated topology against thousands of alternative configurations, converging on the design that minimizes total deployment cost — materials, equipment, civil works — while satisfying every validated constraint.
[FIG 4.A.1] Formalize spatial intent
Design intent — addresses, coverage targets, architecture constraints — is parsed into a formal spatial specification: a structured, human-readable encoding of every node, edge, and constraint the model will reason over.
[FIG 4.A.2] Query the spatial representation
The engine reads directly from a structured representation of the physical environment — street geometry, building footprints, existing infrastructure, and regulatory boundaries — through deterministic path-based queries, not probabilistic search.
[FIG 4.A.3] Prioritize by engineering relevance
Retrieved spatial elements are scored against engineering criteria — route feasibility, infrastructure reuse potential, capacity headroom — filtering the representation to only the highest-relevance inputs for generation.
[FIG 4.A.4] Write to the spatial structure
The model produces design decisions by writing directly into the spatial representation — placing equipment, defining routes, assigning allocations — as structured, auditable operations on the network's formal topology.
[FIG 4.A.5] Verify against physical constraints
Every generated operation is cross-checked against hard physical limits: capacity thresholds, maximum distances, cascade depth, pressure ratings, voltage limits, and compliance standards. Violations are rejected before they propagate.
[FIG 4.A.6] Converge on minimum cost
A mixed-integer programming solver evaluates the generated topology against thousands of alternative configurations, converging on the design that minimizes total deployment cost — materials, equipment, civil works — while satisfying every validated constraint.
Spatial Intelligence
Credit-based pricing. Scale without limits.
Choose your plan, use credits for AI tasks. Every model, every domain — pay only for what you use.
Pro
500 Jax credits included monthly
AI-powered spatial workspace for small teams and individual operators.
Business
Minimum 10 seats · 800 credits/month
Full platform with Opus-level AI for growing teams.
Enterprise
Unlimited Jax credits
Unlimited AI with dedicated infrastructure for large organizations.
All plans include onboarding and training. Extra credits at $0.10 each.
See what Nexma can build for you.
No commitment required. Nothing to install. A free personalized walkthrough with your own project data.
Frequently asked questions
The platform covers four stages: Plan (feasibility, ROI, architecture selection), Design (AI-generated spatial engineering with MIP optimization), Manage (work order generation, crew dispatch, route optimization), and Live (real-time field monitoring, as-built capture, and organizational memory). Domain-specific functionality is delivered through schema-driven plugins — load a different schema and the entire platform transforms.
Key capabilities include autonomous spatial design, voice and vision field guidance in 80+ languages, mathematical optimization (MIP, VRP, constraint programming), and integration with 100+ real-time intelligence feeds. Every output is fully editable — your engineers remain in control.
Each domain is defined by an Ontology Schema — a structured definition of entity types, relationships, constraints, and engineering rules. Loading a new schema transforms the entire platform: different map layers, different AI tools, different engineering validations. Zero code changes required.
Each organization's data is isolated in a dedicated tenant with role-based access controls. You can export or delete all your data at any time. For enterprises with on-premise requirements, we offer private deployment options.
Nexma combines all three capabilities — data fusion, geospatial visualization, and AI-powered engineering — in a single platform with an autonomous AI agent (Jax) that can design, optimize, and validate infrastructure across any domain.
Pro starts at $200/seat/month with 500 credits. Business is $150/seat (min 10 seats) with 800 credits and Opus model access. Enterprise is custom with unlimited credits. Extra credits are $0.10 each, togglable per seat. Premium data feeds are available as add-ons.
Still have questions?
Our engineering team is happy to walk you through the platform.