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Artificial Intelligence Product Specification (AIPS)

An open specification for packaging, describing, and governing AI Products — from models to agents to future intelligent systems.

Read the FrameworkDeployment (AIPDS)Semantics (AIPROD)Passport (AIDPP)

Product Thinking in AI

The AI era requires more than building models or exposing APIs — it requires treating AI capabilities as enduring products. A product is not just an asset; it embodies purpose, accountability, and lifecycle stewardship. Product thinking reframes AI from isolated artifacts into governed, discoverable, and consumable services. Without this framing, organizations risk deploying brittle models without transparency or reusability.

What AIPS Is

The Artificial Intelligence Product Specification (AIPS) provides a unifying grammar for defining and governing AI Products. It does not prescribe how to train or optimize models. Instead, it defines what makes an AI artifact a product : clear identity, capability description, input/output ports, lifecycle stage, governance policies, and economic/resource models. These elements ensure that AI can be deployed, monitored, and reused in ways consistent with enterprise and regulatory expectations.

AIPS is composed of two complementary specifications: AIPDS (AI Product Deployment Spec) and AIPROD (AI Product Semantic Spec). Together they define both the operational footprint and the semantic meaning of AI Products, aligning with discovery platforms, governance processes, and self-service consumption.

Why AIPS Matters

AIPS elevates AI work from experimentation into sustainable practice. It creates a structured way to describe AI Products that regulators, auditors, engineers, and business users can all understand. It ensures that models, agents, and composite systems are documented with rigor, discoverable with clarity, and governed with accountability.

Practically, AIPS supports discovery via trustworthy catalogs, interoperability across platforms, and explainability for stakeholders. It enables compliance with emerging frameworks such as the EU AI Act, while also giving developers a portable way to share AI Products across ecosystems. Where ad hoc model cards and API docs fall short, AIPS provides a standardized, and enduring baseline for AI productization.

In short, AIPS defines the difference between an AI asset and a true AI product. It is a foundation for AI governance, interoperability, and self-service that will evolve with — and anticipate — future AI paradigms, including agentic and generative intelligence.

Core Pillars of AIPS

The AI Product Specification is designed for clarity, governance, and reuse in AI ecosystems. These pillars articulate its intent and guide its adoption across domains.

Product-Centricity

Moves AI from assets to products with lifecycle, accountability, and stakeholder value.

Self-Service

Enables discoverability and consumption via marketplaces and registries, supporting scale.

Governance

Policies, risk classification, and lifecycle states are built-in — supporting compliance and trust.

Interoperability

Ensures AI Products can be compared, exchanged, and extended across platforms and standards.

Quick Access

Framework Introduction

Start with definitions, principles, and scope that distinguish AI assets from AI Products.

Open

AIPDS (Deployment)

See how AI Products are packaged, deployed, scaled, and monitored in real-world environments.

Open

AIPROD (Semantics)

Explore semantic descriptors: identity, lineage, policies, and explainability metadata.

Open

Glossary & Taxonomy

Shared vocabulary of AI Product terms, abbreviations, and classification schemes.

Open

AIPS Model (Overview)

AIProduct at the center, extended with AIPDS for deployment and AIPROD for semantics. KnowledgeSources, Services, and Distributions connect through clear ports and governance.

Citation & Roadmap

“AIPS defines the attributes and semantics that distinguish an AI Product from an AI asset, providing a foundation for governance, reuse, and interoperability.”

— AIPS Charter

Current Focus

  • Initial release of AIPDS and AIPROD v0.1
  • Reference model and serialization bindings
  • Governance and contribution workflow

Next

  • Worked examples across model, agent, and hybrid AI Products
  • Conformance tooling and validation shapes
  • Integration with marketplaces and discovery APIs