What the system is made of.

Product Intelligence

Understand what an AI product actually does.

Each product is described as structured data rather than marketing copy: the task it performs, what it takes in, what it produces, where it runs and what it costs.

• —A shared taxonomy of tasks and product types
• —Structured profiles with inputs, outputs and deployment model
• —Every claim labelled with its source: self-reported or verified

Builder Infrastructure

A structured way for builders to represent their product.

Builders describe their product once, in a format the rest of the system can read. The same representation later feeds distribution surfaces and APIs.

• —Builder onboarding and organization accounts
• —Listing lifecycle from draft to review to published
• —Moderation so listings meet a consistent standard

Contextual Discovery

From keyword lists toward task and intent.

Search over structured product data works in the private build. The harder step, matching a described problem to the products that solve it, is still an experiment.

• —Full-text and fuzzy search across structured listings
• —Side-by-side comparison on the same fields
• —Intent-based matching: experimental, not public

Distribution

Place AI products where relevant users already are.

A product page on one website is a single surface. Distribution means representing a product wherever relevant demand shows up, including inside other tools and agents.

• —Public product profiles
• —Distribution surfaces beyond a single site
• —Placement driven by relevance, not by who shouts loudest

Analytics

Show builders where discovery and adoption happen.

Builders currently see traffic, not context. The goal is to show which tasks, surfaces and audiences lead to a product actually being used.

• —Discovery by surface and by task
• —Drop-off between being found and being used
• —Signals that feed back into better matching

Integrations

Connect AGENYRA to the tools builders already use.

Product data should not be re-entered by hand. Integrations would keep profiles in sync with the product itself.

• —Sync product metadata from existing sources
• —Launch and changelog updates flowing into profiles

APIs

Programmatic access to the product graph.

Once representation and discovery are stable, other applications and agents should be able to query them directly instead of scraping websites.

• —Read access to structured product data
• —Discovery endpoints for agents and applications
• —Write access for builders managing their listings

Network Effects

More products and users make every match better.

Each product added improves coverage. Each use improves matching. Whether that loop can be built without degrading into a ranking game is an open question.

• —Coverage that improves as builders join
• —Matching that improves from real outcomes
• —Guardrails against pay-to-rank dynamics