Grocery Data Intelligence: Smart Grocery Planning with Data + AI

Brahmareddy — Sun, 17 May 2026 12:40:07 GMT

Hi Databricks Community,

For the DAIS 2026 Community Virtual Contest, I built a project called Grocery Data Intelligence. This is a Smart Grocery Planning solution with Data + AI, built using Databricks Free Edition.

The idea came from a very simple real world problem. Grocery stores deal with two common challenges every day. Some fast moving products go out of stock when customers need them. At the same time, fresh products may expire before they are sold. One problem creates missed sales. The other creates food waste. Both problems affect store operations, customer experience, and business value.

I wanted to build a simple solution that helps store teams move from data to decisions and from decisions to actions. Instead of only showing reports, the goal was to help the store understand what needs attention today.

In Databricks, I created a lakehouse flow with Bronze, Silver, and Gold layers. The Bronze layer stores raw grocery data such as stores, products, sales, and inventory. The Silver layer cleans and standardizes the data. The Gold layer creates business ready metrics such as average daily sales, days of supply, stockout risk, waste risk, and recommended actions.

For the demo, I generated 5000 sales records so the solution can support different inventory scenarios. The Gold tables and Databricks views power the dashboard, product action page, inventory story page, and the Ask Data + AI experience.

The most exciting part of this project is the Ask Data + AI page. A store user does not need to write SQL. They can ask simple questions like what should I reorder today, which products may go to waste, or which store needs attention first. The app maps the question to approved Databricks backed views and returns a clear summary, recommended actions, supporting data, charts, and trusted data source details.

This makes the solution useful for store managers and business users because the output is not just numbers. It explains what is happening and what action can be taken. The recommendation may be reorder now, monitor closely, promote soon, discount, or donate.

I also added an Inventory Story page to explain the current business situation in simple language. This helps users understand the bigger picture without going through multiple reports. The Methodology page explains the complete flow from raw grocery data to Bronze, Silver, Gold, Databricks views, Data + AI assistant, and final business actions.

This project helped me think deeply about how Databricks can power practical Data + AI applications beyond traditional dashboards. A clean Gold layer makes insights reusable. Views make the frontend easier to connect. Storytelling makes analytics easier for business users to understand.

My goal with this project is simple. Help grocery stores plan better, reduce waste, avoid stockouts, and make faster inventory decisions using trusted data.

Demo video: https://youtu.be/rU1ceOf51_A

App url - https://grocerydataintelligence.com

I would love to hear your feedback, suggestions, and ideas from the community.

Thank you,

Brahma

Re: Grocery Data Intelligence: Smart Grocery Planning with Data + AI

AshokkumarG — Fri, 22 May 2026 19:09:57 GMT

Hi Brahma,

Really impressive work! The problem framing alone sets this apart — stockouts vs. waste as two sides of the same inventory problem is exactly the kind of clarity that makes a solution easy to champion internally at a store level.

A few things I genuinely liked:

The decision to route natural language questions to pre-approved Databricks views rather than letting an LLM write raw SQL is a smart, production-minded guardrail. A lot of AI demos skip that entirely.

The Inventory Story page is an underrated feature. Translating Gold layer metrics into plain-language narrative for business users is harder to do well than it looks, and it makes the whole solution accessible beyond the data team.

Using the Gold layer to power four different surfaces (dashboard, product actions, story, and the AI assistant) shows real architectural thinking about reusability.

One thing I'd love to understand better — how does the question-to-view mapping work under the hood? Is it semantic similarity, keyword routing, or LLM function calling? That feels like the most interesting technical piece and the write-up leaves it a bit open.

Great submission overall. This is the kind of practical, well-scoped Data + AI application the community needs more of. Good luck!

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Grocery Data Intelligence: Smart Grocery Planning with Data + AI

Re: Grocery Data Intelligence: Smart Grocery Planning with Data + AI