New release unifies demand forecasting, decision optimization, and AI agent orchestration into a single memory-driven system that gets sharper and more tailored to each retailer with every merchandising decision made.
intelo.ai, the AI platform for retail merchandising, announced the latest release of Merchant AI, its integrated system for demand forecasting, assortment planning, and inventory decisions. The release brings intelo.ai’s demand model, decision-optimization engine, and AI agent orchestration together with a persistent memory layer, so recommendations become more accurate and more attuned to each retailer’s business the more the platform is used.
Retail merchandising teams routinely rebuild the same forecasts, size curves, and allocation plans from scratch each season, and the judgment senior planners develop over years often lives only in their own heads and spreadsheets. When that expertise leaves with a planner, or simply doesn’t scale across a growing store fleet, the result is avoidable stockouts, overstocks, and slower, less consistent decisions. Merchant AI is built to close that gap by treating every planning decision as a durable, learnable asset rather than a one-time answer.
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What’s New
The latest release brings five capabilities together into a single closed-loop system for the first time:
- A proprietary demand model that learns true demand for every product, size, and location by correcting for stockouts and accounting for cannibalization and halo effects between products.
- A constrained-optimization solver that turns those forecasts into concrete allocation, buy, and rebalancing decisions that respect a retailer’s real-world limits — inventory on hand, vendor minimums, and financial targets.
- AI agents that reason over forecasts and solver outputs the way an experienced merchandising team would, applying playbooks and guardrails tuned to each retailer’s own practices.
- A memory layer that captures every decision and outcome and feeds it back into the system, so recommendations improve continuously from a retailer’s own history.
- Full explainability, so merchants and planners can ask why any recommendation was made, and challenge, adjust, or override it in real time — with every override becoming a new signal that sharpens future recommendations.
The effect compounds over time: the longer a retail team uses Merchant AI, the more its recommendations reflect that team’s own products, stores, and customers — while every retailer on the platform also benefits from patterns learned across intelo.ai’s broader base of retail deployments.
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