In today’s data-driven retail environment, the significance of data insights cannot be overstated. Each transaction, customer interaction, and digital engagement generates valuable data that can be leveraged to provide better consumer experiences or services. Despite this potential, the multitude of data points generated from shopper behaviors often remain underutilized, as retailers lack the tools necessary to easily extract actionable insights from this vast array of information. In this complex environment, retailers confront two principal challenges:

  • Real-time Data Retrieval: The need for instantaneous access to transactional data from their Order Management System (OMS) is critical for customer service representatives, marketers, and store managers. Whether addressing disputes, discerning purchasing trends, or tailoring marketing strategies, the ability to swiftly retrieve specific user transaction information is indispensable.
  • In-depth Analytics: Moving beyond mere data retrieval, there is a growing imperative to interpret, decipher, and extract insights from these transactions. The retail sector requires an intuitive approach to conducting analytics, free from overwhelming complexity, ensuring a seamless process for gaining meaningful insights.

The GenAI (generative AI) Retail Chatbot is a tool that not only bridges the data retrieval gap but also harnesses the power of advanced analytics, all via a simple chat interface. The innovative Gen AI technology allows retailers to request data interpretations and custom analytics using an intuitive chat interface, transforming complex datasets into actionable insights with unprecedented ease and speed.

Using TrackIt’s GenAI ChatBot

Our newly developed chatbot brings a blend of simplicity and sophistication to data analysis, much like the conversational ease of ChatGPT. It is designed to be incredibly intuitive, making it easy for anyone to use, regardless of their tech background. Just like having a chat with a friend, you can interact with the chatbot in a natural, straightforward manner, turning complex data into powerful insight. Whether exploring deep data sets or seeking strategic advice, this chatbot makes the process seamless and engaging, allowing advanced data analysis to be as simple as everyday conversation.

Use Case 1: For individuals in sales or marketing roles, identification of sales trends is essential. The TrackIt Chatbot significantly simplifies this task. Designed to generate insightful graphs, this tool assists in analyzing patterns swiftly and effectively. Whether the need involves understanding consumer behavior, tracking product performance, or identifying market shifts, this chatbot facilitates the process. The capability to create visual data representations instantly enables informed decision-making, ensuring strategies remain agile and data-driven.

Example Questions: 

  1. What are the most popular products and sales volume for each product in 2022?
  2. For each month of 2022, give me the total revenue, total orders refunded, and net worth as a bar chart.
  3. Give me the market shares of each type of clothing for the year 2021.
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pie chart gen ai retail chatbot screenshot

Use Case 2: Acknowledging the linguistic diversity of global teams, our chatbot is designed to recognize and engage in the user’s language, with French being a prime example. This functionality positions it as an essential tool for ensuring smooth and inclusive communication throughout diverse team settings.

Additionally, for each query, the chatbot provides not only an answer but also reveals the specific code used to retrieve information from the database. This feature enables individuals to understand and potentially utilize the code in other applications, enhancing both learning and practical application.

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The analytics component of the GenAI Chatbot transforms complex data into meaningful visual representations that are easy to understand. As illustrated in the examples above, the chatbot condenses extensive datasets into sophisticated charts and graphs that encapsulate key business metrics and trends. The tool is capable of delivering these rich visualizations almost instantaneously, facilitating prompt and informed decision-making.

Gen AI Retail Chatbot Workflow

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The workflow is designed to streamline the complex process of data transformation, from the moment it is collected to the point where actionable insights are delivered. The following is a step-by-step breakdown of the workflow:

  1. User Request Initiation: The user sends a request to the Retail Chatbot.
  1. Schema Evaluation by LLM: The system’s Large Language Model (LLM) evaluates whether the current database schema can fulfill the user’s request.
  2. SQL Query Generation: Upon approval from the LLM, the system proceeds to craft an SQL query to retrieve the relevant data.
  3. SQL Query Validation by LLM: The generated SQL query undergoes a thorough check by the LLM that ensures alignment with the user’s initial query and addresses any  syntax-related errors. Discrepancies prompt the system to loop back to the query generation phase.
  1. SQL Query Execution: Once validated, the query is executed by the system. If issues arise, the entire process is reinitiated with a capped retry mechanism at five attempts to prevent infinite loops.
  1. Data Visualization and Explanation: After successfully fetching the data, the chatbot determines the optimal visualization method – be it a bar graph, pie chart, or line diagram. The system then generates a visual representation accompanied by a concise textual explanation of the results. Any issues in this phase trigger a retry, again limited to five attempts for efficiency.

Tech Stack Overview

  1. Amazon Bedrock: Serves a dual purpose. The anthropic (pertaining to human-like intelligence) Claude 2 model manages intensive tasks such as SQL generation and data handling, ensuring process efficiency  Claude Instant is leveraged for lighter, more straightforward tasks, maintaining an effective workflow and controlling operational costs.
  2. Xstate: Used to orchestrate the workflow. While Langchain could be a potential alternative, its compatibility with Bedrock is still in the optimization phase, making Xstate the more reliable choice for the current build.
  3. NestJs: Serves a dual-purpose asset in the tech stack, NestJs addresses requirements for both API handling and backend operations.
  4. Amazon API Gateway: Acting as the nervous system alongside Bedrock as the brain, API Gateway facilitates seamless communication between the chatbot and the OMS.

Whenever a transactional detail is sought, API Gateway ensures that the data is fetched securely, efficiently, and in real-time from the OMS, making the chatbot’s response swift and accurate.

  1. Amazon DynamoDB: Every query, result, and interaction needs efficient storage, management, and retrieval. DynamoDB, a NoSQL database service provides the chatbot with the necessary speed and scalability to ensure seamless, quick, and reliable data handling. 

Conclusion: Charting a New Path in Retail Data Practices

This Retail Chatbot represents a significant step forward in the way retailers engage with, retrieve, and interpret data. Its user-friendly design ensures data accessibility for individuals with varying technical expertise. The Chatbot seamlessly integrates data retrieval and advanced analytics, creating a unified platform where data is not only readily available but also intricately woven into the fabric of decision-making.

Link to the marketplace offer: 

Github Repo:

About TrackIt

TrackIt is an international AWS cloud consulting, systems integration, and software development firm headquartered in Marina del Rey, CA.

We have built our reputation on helping media companies architect and implement cost-effective, reliable, and scalable Media & Entertainment workflows in the cloud. These include streaming and on-demand video solutions, media asset management, and archiving, incorporating the latest AI technology to build bespoke media solutions tailored to customer requirements.

Cloud-native software development is at the foundation of what we do. We specialize in Application Modernization, Containerization, Infrastructure as Code and event-driven serverless architectures by leveraging the latest AWS services. Along with our Managed Services offerings which provide 24/7 cloud infrastructure maintenance and support, we are able to provide complete solutions for the media industry.

Additional Resources

An Introduction to Generative AI on AWS

AWS Resources for Generative AI

Amazon Q – Generative AI-Powered Assistant

5 Generative AI Use Cases for Retail and E-Commerce

7 Generative AI Use Cases for Media and Entertainment