our process - How we make catalogues agentic-ready

We start with the product catalogue because that is where agentic commerce is won or lost. Our audit checks GTIN coverage, taxonomy depth, image quality, descriptions, pricing, stock freshness, and merchant-feed readiness against the standards AI agents already use to rank products.

You receive a prioritised remediation plan that shows which gaps block recommendation eligibility, which fixes protect revenue first, and which data owners need to be involved. The output is practical: a catalogue-readiness score, a risk register, and the fastest path to richer product data.

Once the gaps are clear, we build the pipelines that make catalogue data durable. ERP, PIM, POS, and inventory signals are normalised into product records that AI agents can read, compare, and trust without waiting for manual spreadsheet clean-up.

Our enrichment layer adds the content agents need: complete attributes, clearer descriptions, better categorisation, and trend-aware language that still sounds like your brand. Every field stays traceable, so merchandising teams can review the data before it reaches a live feed.

We keep implementation narrow and measurable. The work is organised around catalogue coverage, freshness latency, feed acceptance, and the specific product categories with the highest commercial impact.

A catalogue is only agentic-ready if it stays current. We help teams operate the new data layer in production, with monitoring for stale inventory, rejected feed fields, missing product identifiers, and content that drifts away from live search demand.

Trend signals are folded back into the catalogue, so products can respond to cultural demand, regulatory news, and seasonal shifts while staying accurate. The goal is a living product data layer that strengthens every recommendation surface.

We continue measuring whether the catalogue performs at scale across agentic shopping entry points, merchant feeds, and internal commerce systems.