AI Economics Joins NVIDIA Inception
AI Economics has been accepted into NVIDIA Inception, NVIDIA’s programme for startups building advanced AI products.
We applied with MVP Fabric – AI SDLC, the platform AI Economics brings to market for controlled, traceable and policy-aware software delivery. Membership supports our work on private AI inference for regulated enterprises.
From requirements to controlled deployment
In regulated environments, AI projects can lose direction during delivery. Business requirements, architecture, security policies, regulatory obligations and implementation decisions drift apart as work progresses. MVP Fabric keeps these elements connected. It runs the full delivery path, from requirements to deployment, as one controlled process. Enterprise standards become reusable delivery rules. AI agents operate within those rules, while authorised people approve consequential decisions. The resulting evidence trail records what was proposed, approved, changed and deployed.
For a well-defined use case, our target is a working proof of concept in three days.
Private inference as the next stage
NVIDIA Inception provides access to technical resources, training, expertise and the wider NVIDIA ecosystem. We will use these resources to evaluate the next stage of the MVP Fabric architecture: private inference for governed software delivery.
MVP Fabric currently runs on external model APIs. Our first planned experiment adapts a smaller language model for requirements engineering and tests it on GPU infrastructure. We will compare it with a general-purpose model on the same held-out evaluation dataset, under the same enterprise rules and human review process.
We will measure requirements completeness, incorrect assumptions, the number of human revisions, time per accepted specification and cost per accepted specification. We are testing whether a specialised model can improve the quality and efficiency of enterprise requirements engineering while supporting stronger privacy and control. We will publish capability claims only after the results have been measured.
What this enables
The work expands the AI SDLC technical roadmap and creates more options for private, enterprise-grade AI infrastructure. For regulated organisations, private inference may reduce dependence on external model providers and strengthen control over data, model behaviour and deployment environments.
Our mission is unchanged. We help organisations decide which AI initiatives are worth building, then deliver them under rules that management, engineering, security and compliance teams can defend.
Next step
We are preparing the first requirements engineering benchmark and scoping the GPU infrastructure needed to run it. If your organisation is building an AI application in a regulated environment, we would like to hear about the controls, evidence and deployment constraints you face.
Discuss a use case with AI Economics
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