Retail AI Engineering

AI Engineering for Retail & Consumer Brands

XPndAI builds AI agents, demand forecasting models, in-store automation, and customer engagement tools for retail chains, supermarkets, and consumer brands.

What We Build
For Your Business

End-to-end AI and software engineering with measurable enterprise outcomes.

How Leading Teams
Deploy This Technology

1

Demand Forecasting — Grocery Chain

A regional grocery chain deployed our demand forecasting AI across 45 stores. The model predicts daily SKU-level demand with high accuracy, automatically triggering replenishment orders and reducing food waste and stockout incidents significantly.

2

Loyalty AI — Consumer Brand

A consumer goods brand built a personalized loyalty engine on top of their customer purchase data. The AI segments customers, predicts churn, and triggers personalized offers via WhatsApp — improving repeat purchase rates across their user base.

Built With Modern Tools

OpenAIPythonTensorFlowFastAPIPostgreSQLRedisWhatsApp Business APIShopifyAWSDocker

Common Questions Answered

Everything you need to know before starting an AI project with XPndAI.

What AI solutions does XPndAI build for retail companies?

XPndAI builds AI product recommendation engines, smart inventory and demand forecasting, AI customer service chatbots for WhatsApp and website, visual search and AI-powered product discovery, personalised marketing AI, loss prevention and anomaly detection, and in-store AI analytics on footfall and customer behaviour.

How much does retail AI development cost?

Retail AI at XPndAI starts from ₹6–12L for an AI chatbot or recommendation widget, ₹20–60L for a demand forecasting or personalisation engine, and ₹75L–2Cr for an enterprise retail intelligence platform. All pricing is transparent and fixed-fee.

Can AI help reduce out-of-stock and overstock situations in retail?

Yes. XPndAI demand forecasting AI uses historical sales data, seasonality, promotions, and external signals (weather, events) to predict demand at SKU-store level with 85–92% accuracy. Clients typically see 20–35% reduction in stockouts and 15–25% reduction in excess inventory carrying costs.

How long does retail AI implementation take?

A product recommendation engine MVP takes 6–8 weeks. A demand forecasting model with POS data integration takes 10–14 weeks. An enterprise retail AI platform with ERP and POS integration takes 4–6 months. XPndAI provides a pilot on one product category before full rollout.

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Deep Dive. Free.

Talk directly to our lead engineers. We audit your requirements, propose the exact architecture, and give you a transparent roadmap — all in one call.

  • Technical Architecture Blueprint — custom for your use case.
  • Scalable Infrastructure — built for enterprise growth.
  • Production-Ready Code — rigorous QA and deployment.
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