Neon AI Factory Architect Is Available for Download

Building an AI Factory is not simply a matter of buying GPUs.

The difficult part is turning a workload requirement into a coherent, defensible architecture across:

GPU compute
scale-up and scale-out networking
storage
management and control planes
security
orchestration
observability
facilities assumptions
deployment phasing
BOMs
architecture evidence
customer documentation

… and doing it quickly enough to keep pace with the market.

That is the problem Neon AI Factory Architect was built to address.

The problem: AI Factory architecture does not scale manually

The industry has standardized many of the building blocks.

We increasingly know what the GPUs look like and we know the vendor reference architectures.

We know the network technologies and we know the storage platforms.

We know the orchestration stacks.

Yet designing a customer-specific AI Factory still involves a surprisingly fragmented engineering process.

Requirements arrive through RFIs, RFPs, workshops, spreadsheets and conversations.

Architects then translate those requirements into compute sizing, fabric design, storage assumptions, security zones, software architecture, bills of material and diagrams.

Each artifact is frequently produced separately. Each revision creates another opportunity for inconsistency.

This leads to problems not only on “Day 1” (engineering and first deployment) but in the even more critical “Day 2” (production operation post go-live).

Every architecture eventually confronts the same question:

Can we demonstrate why this design is correct?

Neon approaches that problem differently. The rest of this article will describe how and why.

See Neon in 30 seconds

Rather than describe the product first, get an overview of what it does.

30-second Neon overview

The proposition is simple:

Turn your customer requirements into an engineered AI Factory architecture with the reasoning, evidence and deliverables attached.

What is Neon?

Neon AI Factory Architect is an architecture engineering environment for designing GPU infrastructure and AI Factories.

It connects the stages that are usually handled independently:

Customer requirements with derived workload leading to compute (GPU count) + network + storage (high performance, object, block) + orchestration platform + security + physical realization leading to architecture documentation and design basis leading to evidence.

The aim is not merely to draw beautiful architecture diagrams.

It is to make the architecture itself computable, inspectable and traceable back to the requirements and aligned to the vendor reference architectures.

Neon can reason across infrastructure alternatives, maintain architectural dependencies, generate engineering artifacts and preserve the relationship between a customer requirement and the resulting design decision.

With receipts.

Why should a CTO care?

Because AI infrastructure is becoming too expensive and too complex to engineer through disconnected spreadsheets, diagrams and tribal knowledge.

For a CTO, the value proposition is not “better diagramming.”

It is:

Faster architecture decisions.
More consistent engineering.
Greater reuse of institutional knowledge.
Earlier identification of design problems.
Better evidence behind investment decisions.

The objective is to shorten the path between:

We need an AI Factory, like yesterday!

and

Here is the architecture, why it works, what it costs, how it scales and what still needs to be verified.

Twelve architecture questions that come up in practice

Neon is designed around the questions that repeatedly arise during an AI Factory programme.

For example:

What workload are we actually designing for?
Which GPU architecture is appropriate?
How many GPUs and nodes are required?
What is the correct scale-up architecture?
What scale-out network is required?
Is the fabric genuinely non-blocking?
What storage capacity and performance are required?
How should management and control-plane infrastructure be separated?
What security zones and trust boundaries exist?
How does the design evolve from pilot to production scale?
What equipment, ports, links and media are physically required?
What evidence demonstrates that the architecture satisfies the original requirements?

Those are not twelve isolated diagrams or solution documents. They are twelve views of one architecture model. Twelve potential design programmes.

Different users need different views of the same architecture

One of the problems with conventional architecture tooling is that everybody gets essentially the same output.

However, the CTO, salesperson and solution architect are asking fundamentally different questions.

For the CTO

Neon helps answer:

Is this architecture credible?
What are the major design choices?
Where are the risks?
How does it scale?
What assumptions remain unresolved?
What investment is required?

For Sales

Neon helps answer:

Can we respond to the customer's RFI or RFP quickly?
What architecture can we confidently propose?
What can we demonstrate rather than merely claim?
Can we produce a coherent solution pack for the customer?

For the Solution Architect

Neon helps answer:

What are the dependencies between compute, networking and storage?
Are the port counts actually feasible?
Which assumptions are governed versus uncertain?
Can the physical implementation be derived from the logical architecture?
Can every significant decision be traced back to the requirement that caused it?

The value comes from allowing all three personas to work from the same underlying architectural truth.

The five-minute executive tour

For executives, investors, partners and technology leaders who want the product explained without going into engineering depth:

Watch the 5-minute Executive Overview

This covers the business problem Neon addresses, how the architecture workflow operates and why an AI Factory benefits from a governed design system.

Eight-minute product walkthrough

For architects, engineers and technical leaders who want to see more of the system itself:

Watch the 8-minute Product Walkthrough

The walkthrough demonstrates how Neon moves from customer inputs into architecture views and engineering evidence.

From RFP to architecture

The most useful test of any architecture product is not whether it can produce an attractive demo. It is whether it can work through a realistic customer problem.

We therefore used Neon against an AI Factory procurement scenario based on a real-world case study we conducted. Starting with customer requirements, the process develops the architecture across:

The B300 GPU Compute Estate
Multi-Fabric Networking (E-W, N-S, Storage, OOB)
High Performance AI Storage
Security
GPU server orchestration
Out of Band Management
Multiple deployment phases (cluster scaling)
Physical realisation
Customer documentation

The result is not simply a recommendation. It is an architecture with a visible chain of reasoning from architecture to refined visualisation.

View the worked RFP to Architecture case

Download the Architecture Solution Pack

For those who want to inspect real output design documents rather than the interface, we have also produced a representative architecture solution pack based on a real-world deployment case Neon engaged in.

It shows the type of customer-facing artifact that can emerge from the Neon workflow.

Download the Architecture Solution Pack:

Open the Architecture Solution Pack download page

Individual documents:

The pack contains:

A typical RFI and RFP as seen in the industry
Neon’s generated executive summary of the architecture
Neon’s solved full solution architecture proposal
Neon’s solved security architecture and findings
Neon’s project planning document for input to the delivery process
The final solution architecture proposal presented to the customer by the RFP response team
An end-to-end GUI walkthrough of the Neon workflow
The product datasheet

Ten things Neon can demonstrably do

We deliberately use the word demonstrably. AI products are very easy to describe in terms of what they could theoretically do.

We wanted a harder standard, and so for every customer build-out, we asked:

Can the capability actually be demonstrated through the product on this actual project?

Neon can demonstrate the ability to:

Explore multiple AI Factory architectures and topology variations.
Compare competing infrastructure technologies.
Translate customer requirements into structured architecture decisions.
Size GPU compute against workload requirements.
Design scale-up and scale-out network fabrics.
Develop storage architectures alongside the compute design.
Represent security zones, trust boundaries and crossings.
Model phased expansion from initial deployment to production scale.
Generate physical infrastructure realization and engineering evidence.
Produce customer-facing architecture documentation while preserving requirements traceability.

See the evidence behind the 10 capabilities:

Neon AI Factory Architect — Customer FAQ

The bigger picture

Neon began with a simple observation: the industry is about to build an enormous amount of AI infrastructure.

Much of the underlying hardware will be standardized.

The competitive advantage will increasingly come from how quickly organizations can transform requirements into reliable architectures and those architectures into deployed capacity.

That means architecture itself needs better design tooling.

Not simply drawing tools. Not Schneider EcoStruxure, not Autodesk Revit, not Cadence, not Ansys.

Not simply configuration tools and definitely not an AI chatbot that produces plausible diagrams.

What is needed is an environment in which architecture can become a governed engineering process and deliver a complete AI Factory IT architecture on top of your physical datacenter design.

That is the direction we are pursuing with Neon.

Evaluate Neon

Neon AI Factory Architect is available for evaluation with selected customers, technology partners, infrastructure providers and engineering organizations.

If your organization designs, sells, procures or operates GPU infrastructure and AI Factories, we would be interested in putting Neon against a real architecture problem.

Neon also offers optional training and consultation courses on AI Factory Architecture and delivery based on the AI Factory Architect suite.

Neon AI Factory Architect

From requirements to architecture.

From architecture to evidence.

From bare metal to token.

Discuss deployment with Neon AI Cloud