arnit turns enterprise catalog data into contextual, agent-ready commerce feeds. It enriches the product information agents need, compiles it for protocols and AI discovery surfaces, and evaluates how products appear across real shopper missions. The resulting loop helps retailers improve AI visibility today and enable agent-led actions as the ecosystem matures.
Product feeds were designed to distribute records to known channels. AI agents reason over shopper intent, product meaning and live commercial conditions. Retailers now need to engineer what agents can understand, test how that information performs and improve it continuously.
Core fields may satisfy a channel specification while leaving agents unable to reason about fit, use case, compatibility, occasion, trade-offs or the questions a shopper is likely to ask.
A valid feed or endpoint makes participation possible. It does not show whether an agent can find the right product, interpret it accurately, compare it fairly or select it for a relevant shopper mission.
AI discovery, recommendation and transaction experiences will evolve across platforms. Retailers need a continuous way to evaluate product visibility, diagnose gaps and refresh the context supplied to agents.
Yarnit treats agentic commerce readiness as an operating system, not a one-time feed export. The same workflow builds what agents consume, tests how they interpret it and uses the results to improve the next version.
Yarnit connects data readiness, feed engineering, protocol enablement and continuous evaluation in one agentic commerce system.
Assess catalog completeness, contextual depth, freshness, structured data, endpoint readiness and the actions currently available to external or owned agents
Create richer product objects from enterprise data, imagery, taxonomy and market signals. Generate the attributes, relationships and answerable content required for intent-led discovery.
Compile governed commerce data for ACP, UCP and other structured discovery surfaces. Maintain destination-specific mappings without fragmenting the source product truth.
un representative shopper missions through the available feeds and agent experiences. Test retrieval, accuracy, relevance, comparison quality, grounding and action readiness.
Monitor where the brand and its products appear, which products agents select or cite, how competitors are represented and which intent spaces remain uncovered.
Expose approved capabilities for product search, product detail, cart handoff or checkout as the retailer's systems and chosen protocols permit. The same foundation can power an owned AI Sales Rep.
Bring us a priority category and the agent surfaces that matter to you. Yarnit will assess the feed, simulate real shopper missions and show where context, visibility or action readiness breaks down.
Giving shape to intelligence
