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AI-Powered Retail Insights & Forecasting POC

Built an LLM-driven chatbot and Amazon Forecast pipeline that helped a multi-branch supermarket chain analyze sales, predict demand, and generate insights instantly—without manual reporting.

Case Study

AI Chatbot and Demand Forecasting POC for a Multi-Branch Supermarket Chain in Saudi Arabia

A large supermarket chain in Saudi Arabia, operating multiple branches, wanted to improve internal decision-making by leveraging AI for sales insights and demand forecasting. The company relied heavily on manual reports, spreadsheets, and subjective judgment for replenishment planning and performance analysis. To explore the feasibility of AI in their operations, they required a practical proof-of-concept (POC) combining conversational analytics and predictive forecasting.
Vivid Soft was engaged to design, engineer, and deliver a complete AI-powered POC tailored to the supermarket’s internal datasets, workflows, and business needs.

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The Challenge

The client faced several operational challenges that made AI adoption valuable:

Fragmented and Inconsistent Data

Sales and inventory data varied across branches, SKUs, categories, and seasons. Data quality issues required careful preprocessing.

  • Data inconsistencies across branches
  • Mismatched sales and inventory records
  • Variations across SKUs and categories
  • Seasonal fluctuations not standardized

Manual, Time-Consuming Reporting

Store managers spent significant time generating reports, comparing numbers, and preparing summaries for weekly meetings.

  • Reports created manually every week
  • Time-consuming data gathering
  • No automated summaries or insights
  • High effort for simple comparisons
  • Slower decision-making due to delays

Lack of Predictive Insights

All forecasting was manual. There were no predictive models for demand, seasonal trends, or stock-out risks.

  • No automated demand forecasting
  • No visibility into seasonal trends
  • Manual stock-out risk assessment
  • Limited data-driven decision support
  • Reactive planning instead of proactive insights

Need for Natural-Language Interaction

Leadership and branch managers needed a way to ask questions in plain language, such as:

  • Which branch performed best last week?
  • Which items will be in high demand next month?
  • Show me declining categories this quarter.
  • Traditional dashboards could not offer this level of interactive insight.
Our Approach

Our Solution

Vivid Soft designed and implemented a unified POC combining an AI-driven chatbot with an AWS-based forecasting pipeline, enabling the supermarket to interact with its data more intuitively and to predict item-level demand.

AI Chatbot for Sales and Inventory Insights

The chatbot allowed internal teams to query their data conversationally.

Capabilities included:

  • Understanding natural-language questions
  • Generating structured queries behind the scenes
  • Summarizing trends by branch, category, and SKU
  • Identifying anomalies and top or low performers
  • Providing explanations in clear, simple language

Technologies: LLM APIs, embeddings retrieval, secure query interface.

Demand Forecasting Using Amazon Forecast

We developed a forecasting pipeline using historical sales data.

Key steps:

  • Data cleaning and preprocessing
  • Feature extraction for seasonality and holidays
  • Model experimentation using Amazon Forecast
  • Metrics evaluation (MAPE, RMSE)

The output included item-level predictions, branch trends, and anticipated stock-out risks.

Combined POC Interface

We delivered a lightweight web interface where internal users could:

  • Upload sales datasets
  • View forecast visualizations
  • Interact with the chatbot
  • Export summaries for planning meetings

AI Quality and Safety Controls

To ensure reliability:

  • Guardrail prompts were implemented
  • Invalid query handling was added
  • Outputs were validated for factual consistency
  • Error analysis workflows were incorporated
Outcomes

Results

These outcomes highlight the measurable business value and technical advancements delivered through the project.

Business Impact

  • Internal teams gained immediate access to insights previously buried in reports.
  • Forecasting became objective and data-driven rather than manual and subjective.
  • The organization identified opportunities to reduce stock-outs and over-stocking.
  • The POC demonstrated the practicality and return on investment of adopting AI in daily operations.

Technical Impact

  • Delivered a working chatbot and forecasting pipeline tailored to the client’s data.
  • Established a clean and modular architecture suitable for future scaling.
  • Provided a clear roadmap for integrating AI into long-term systems.
Summary & Key Insights

Tech Stack

  • AI/LLM: OpenAI and jais-13b Model
  • AWS: Amazon Forecast, S3
  • Backend: Python, Java (Spring Boot)
  • Data: Pandas, NumPy, SQL

Engagement Summary

  • Service: AI-powered POC development
  • Role: AI engineering, forecasting pipeline, backend/API development
  • Client: Multi-branch supermarket chain, Saudi Arabia

Key Takeaways

  • AI can significantly reduce manual reporting effort and improve decision quality.
  • Predictive demand forecasting provides a measurable advantage in inventory planning.
  • Conversational analytics enables faster and clearer access to insights for non-technical users.
  • The POC validated a strong foundation for future enterprise AI initiatives.
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