Relate usage evidence to a shared vocabulary before building measurement systems.
§1 — Definition
A canonical package namespace.
An organized, implementation-neutral starting point for the economics of AI-enabled services.
An organized, implementation-neutral starting point for measuring AI usage, allocating AI-related cost, designing usage-based pricing, evaluating inference monetization and margins, and governing the economic performance of AI-enabled services. The guiding principle is deliberately modest: Publish the map, not the machine.
§2 — The enterprise problem
Spread across teams that rarely share a vocabulary.
- Technical usage
- Infrastructure cost
- Internal allocation
- Product pricing
- Revenue operations
- Finance and governance
§3 — What the foundation provides
An organized reference, ready to build on.
- A canonical package identity
- Four focused capability namespaces
- Validated concept definitions
- Visible package-to-capability relationships
- Machine-readable reference files
- Buyer-controlled implementation flexibility
- A public reference and evaluation starting point
Representative public foundation only. Final transaction scope and terms are defined separately.
§4 — Capability architecture
One canonical package above four peer capabilities.
AI Inference Economics Foundation
AI Usage Metering
Measurement and normalization of AI usage evidence.
Model Cost Allocation
Attribution and allocation of AI-related operating cost.
Usage-Based AI Pricing
Translation of measurable AI usage into pricing structures.
Inference Revenue
Monetization, revenue operations, cost-to-serve analysis, and margin evaluation.
The capabilities are structurally equal peers. The lead commercial capability is not the package anchor and is not hierarchically superior.
§5 — Capability crosswalk
Each capability, its question, and its boundary.
| Capability | Question owned | Boundary | Namespace | Status |
|---|---|---|---|---|
| AI Usage Metering | What was consumed? | Measurement and normalization of AI usage evidence | aiusagemetering.com | Planned Namespace |
| Model Cost Allocation | Who or what drove the cost? | Attribution and allocation of AI-related operating cost | modelcostallocation.com | Planned Namespace |
| Usage-Based AI Pricing | How is consumption priced? | Translation of measurable AI usage into pricing structures | usagebasedaipricing.com | Planned Namespace |
| Inference Revenue | How is commercial AI inference monetized? | Monetization, revenue operations, cost-to-serve analysis, and margin evaluation | inferencerevenue.com | Live Capability |
Planned namespace links become fully active when each corresponding LJP-controlled capability reference is published.
§6 — Economic context
A conceptual economic journey.
- AI activity
- Usage evidence
- Metering and normalization
- Cost attribution and allocation
- Usage-based pricing
- Inference monetization and revenue operations
- Margin and economic governance
- Formal revenue recognition — outside scope
This sequence is conceptual and non-mandatory — not a required implementation sequence and not a proprietary workflow. Each capability is independently valuable and independently deployable.
§7 — Why a shared foundation may help
From scattered inputs to an organized reference.
- Disconnected usage metrics
- Unclear cost ownership
- Inconsistent pricing units
- Weak cost-to-revenue traceability
- Repeated translation between teams
- Shared definitions
- Clearer capability boundaries
- Visible relationships
- Improved cross-functional planning context
- A stable starting point for future machine-readable publishing
Qualitative context only. It does not promise cost savings, compliance, speed, revenue, or adoption.
§8 — Buyer-team relevance
Useful to more than one function.
AI Platform & Engineering
Product Management
Plan features and packaging against organized cost, pricing, and monetization concepts.
FinOps & Cloud Economics
Anchor cost attribution and allocation to consistent, named definitions.
Pricing & Monetization
Design usage-based pricing structures from measurable, well-defined usage.
Revenue Operations
Trace monetization and cost-to-serve concepts back to a common map.
Finance & Controllership
See where inference revenue operations end and the formal accounting boundary begins.
Corporate Development & Strategy
Evaluate the package and its capability structure with a clear map in hand.
Buyer examples are illustrative and imply no relationship.
§9 — Namespace files
Machine-readable reference artifacts.
- ontology.jsonldPackage and capability relationships.
- namespace.jsonA minimal, versioned public namespace manifest.
- llms.txtA concise machine-readable summary.
- robots.txtStandard crawl directives referencing the sitemap.
- sitemap.xmlThe canonical crawlable locations.
§10 — What this does and does not provide
Credibility boundaries.
It is a foundation to build from. It is not an API, agent protocol, billing engine, pricing calculator, software product, standards body, production FinOps platform, or accounting standard, and does not replace engineering, FinOps, product, finance, counsel, accounting, or implementation teams. The implementation, financial policy, product design, accounting judgments, and commercialization remain the buyer’s.
LJP is not affiliated with, endorsed by, or representing the FASB, the IFRS Foundation, or any other standard-setter. Standards references describe conceptual alignment only.
§11 — Acquisition, licensing, and evaluation
An organized starting point, with freedom preserved.
Acquire an organized, standards-aware starting point that reduces foundational work while preserving complete architectural freedom.
AcquisitionLicensingOptionStaged transferPartnershipStructured strategic evaluation
These are possible structures, not standing offers. Specific scope and terms are discussed per engagement.
§13 — Evaluation
Start an evaluation.
Evaluation and package inquiries are handled directly by LJP Asset Group LLC.
Email: support@ljpassetgroup.com · Published by LJP Asset Group LLC