Engineered and deployed for Pasha Fabrics
A retailer with seven stores and a warehouse knows what sold yesterday. This platform tells them what will sell next — which colour will trend and when, which customers are likely to buy it, which shop runs short first, and what's actually on the shelves right now, read by camera. Built on Aliera's MIRAI decision-intelligence engine.
Interface shown is a representative demonstration.
What It Does
Colour & product trend forecasting: predicts when a specific colour or fabric type will be in demand — down to the weeks of the year — from historical sales, seasonality, and live transactions.
Customer profiling: identifies which customers' buying behaviour points to that colour or type, so the sales team can reach out before the trend peaks — not after it passes.
Per-shop stock prediction: forecasts when stock will run low in each of the seven stores individually, and recommends what to order or redistribute — for that specific shop, from the warehouse or a sister branch.
Camera-based shelf intelligence: in-store vision tracks what's on the shelves — counts, colours, gaps — so restocking decisions don't depend on a shop attendant's guess.
How It Works
Managers don't dig through reports — they ask. A conversational layer with voice command sits on top of the analytics, answering questions in natural language and raising alerts before a shortage becomes a lost sale.
Inventory systems, sales transactions, customer records, and live shelf cameras across the warehouse and all store locations.
Demand forecasting per product, colour, season, and location — plus the customer segments most likely to respond.
Stock redistribution, replenishment alerts, and outreach lists — ranked actions, not raw dashboards.
Voice and text queries against the whole business — "which shop runs out of this first?" — answered instantly, with the reasoning shown.
Where It's Used
The deployed system runs a fashion and textile network — but the engine underneath is sector-agnostic. The same platform adapts to supermarkets, pharmacies, electronics, and consumer-goods retail.
Stock levels monitored across warehouse and branches; shortages flagged before they occur, allocation tuned to cut both stock-outs and dead stock.
Procurement and production planning driven by predicted sales — historical trends, seasonal behaviour, and live purchasing patterns.
Segmentation, preference analysis, and buying-trend detection that turn a customer database into a targeted outreach engine.
One operational view of every location: compare performance, monitor inventory, and coordinate stock movement across the network.
AI image recognition on in-store cameras: product availability, empty-shelf detection, and merchandising issues caught as they happen.
Natural-language access to the entire operation — instant answers, summaries, and recommendations, no report navigation required.
Why It Matters
Multi-location retail loses money in the gaps: the shop that ran out while another sat overstocked, the trend spotted a month late, the loyal customer nobody called. This platform closes those gaps — forecasting accuracy up, shortages and excess inventory down, and every location visible from one screen. The result is a network that responds proactively instead of reconciling reactively.
Engineered and deployed by Aliera's AWRTD teams on the MIRAI decision-intelligence engine — research to deployment, integrated with the client's retail management systems.
Platform interfaces shown are representative demonstrations — client data and production dashboards remain confidential under NDA.
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