For CTOs, Ariane is described as not a black-box AI tool but a large language model grounded in a causal Retail Knowledge Map, meaning Ariane RDS reasons within a structured retail knowledge graph rather than from raw unstructured data. The technical architecture includes an AI layer combining Ariane RDS and the Retail Knowledge Map, API and file-based data ingestion, vendor-agnostic ERP and POS compatibility, cloud or hybrid deployment, and a weekly data refresh that runs live during an engagement.
The platform architecture runs through four stages in one continuous loop: Sense, Decide, Execute, and Learn. In Sense, EPOS and other data are ingested and modelled into the Retail Knowledge Map, feeding forecasting and dashboards. In Decide, Ariane RDS generates decision cards, each with a causal diagnosis, a recommended action, and a financial value. In Execute, approved decisions generate executable outputs such as briefs, planogram updates, and CRM campaign briefs, routed to downstream systems. In Learn, decision outcomes feed back into forecast models so every actioned card improves the next recommendation cycle.
Ariane combines a rules-based layer, machine learning, and Ariane RDS, each used in the right place. Threshold detection, anomaly flagging, and priority scoring are deterministic, defined by the retailer's team and the Retail Knowledge Map, so the system is designed to never hallucinate commercial decisions because it is never given open-ended questions against raw data.