• VTP Altitude, Wakad, Pune, Maharashtra 411033, India

Robotic hand reaching toward a glowing neural network, representing AI-driven forecasting
-22% inventory waste Store-level accuracy
  • Client:

    Everline Retail Group

  • Category:

    AI/ML Solutions

  • Services:

    AI/ML Solutions, Software Development, Cloud & DevOps

AI/ML case study

AI-Powered Demand Forecasting

Everline Retail Group was ordering stock based on last year's spreadsheet and a regional manager's gut feel — a method that left some stores overstuffed with slow-moving inventory while others ran out of best-sellers. We built a demand forecasting engine that predicts store-level demand from real sales, seasonality, and local trend data, cutting inventory waste by 22%.

00

%

Less inventory waste, first two quarters

2

Quarters to network-wide impact

The challenge of project

Everline's buying team relied on manual, historical-average forecasts that didn't account for seasonality, local events, or emerging trends. Overstock tied up cash and warehouse space, while stockouts on popular items sent customers to competitors. Every store used the same forecast regardless of its actual local demand pattern.

Demand Forecast Updated
ActualForecast →

Reorder recommended

Store #114 · Seasonal spike detected
Auto

Stockout risk flagged

Store #229 · 5 days of cover left

Network snapshot

  • Stores forecasting live142
  • POS systems synced142
  • Last model refreshToday
Adjusts as trends shift
  • Store-level demand forecasts built from real sales history and seasonality

  • Automated reorder recommendations that adjust as trends shift

  • Buyer dashboard flagging at-risk stockouts before shelves go empty

  • Integrated directly with Everline's existing POS and warehouse systems

The result of project

Inventory waste dropped 22% across the store network within the first two quarters, freeing up warehouse space and cash previously tied up in slow-moving stock. Stockouts on top-selling items fell sharply, and Everline's buying team now spends its time reviewing forecasts instead of building them from scratch every week.

Before the engine

One forecast for every store

With the engine

Store-level, self-adjusting

More completed work

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  • 00

    +

    Years of experience

    Building and supporting business-critical software.

  • 00

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    Projects delivered

    Across web, mobile, cloud and data platforms.

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    Client retention

    Most clients stay on for ongoing support and new work.

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    Specialists on staff

    Engineers, designers and analysts under one roof.