Demo store. Yuktikara is a fictional company from a Microsoft Fabric series. Nothing here is for sale. About this demo · How it’s built

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Under the hood

How Yuktikara runs on data

Yuktikara is a fictional retailer, but its data platform is real Microsoft Fabric, built by hand one episode at a time in the series Yuktikara Store: Microsoft Fabric, End to End. Here’s how a sale in a store, a click on this website and a tag on a shelf become answers an AI agent can give, and which parts exist yet.

33,757orders
69,198order lines
6,749returns
11,773RFID shelf positions

Yuktikara / architecture explorer

From a shop floor to a smarter decision.

Six stages. One connected story. Follow a journey, explore an episode, or trace a component.

Explore the build
The big picture Retail activity becomes data, shared meaning, answers, and action.Select a component to explore ↓
Illustrated data flow · not live telemetryMotion reduced by your device settings
01

Capture

Shops & sensors

02

Prepare

Generate & refresh

03

Store

OneLake & real time

04

Connect

Business meaning

05

Understand

Reports & agents

06

Act

People & workflows

Highlighted connection Available path Planned / in progressA blueprint of the demo and series roadmap

Selected component

Pick a component

Select any box on the map to see what it does, where its data comes from and where it goes next. Or follow one of the journeys above.

Receives from

Select a component to see its inputs.

Passes to

Select a component to see its outputs.

RFID snapshot: . Synthetic data.

One question, four answers

“What were our sales?” sounds simple. The data holds at least four believable answers, and only one of them is right. The gap between the first and the last is 20.3%. The definition belongs in one place, a measure in the semantic model, and the ontology holds the pieces it works on: order status, line amounts, and accepted returns with the day they were accepted.

  1. Gross, every order$13.88M

    Counts cancelled and pending orders, and ignores markdowns and returns

  2. Order total$13.02M

    Only real sales, but includes sales tax

  3. Subtotal$12.03M

    Right orders, no tax, but forgets the returns

  4. ✓ Net sales$11.06M

    Completed and Shipped orders, minus accepted returns on the day they're accepted. No tax

Sales is only the most familiar example. The same data covers buying, stock month by month, promotions and each store’s plan, so it also answers which supplier is letting us down, which shelves need refilling, whether the clearance paid, and which stores are behind plan. A platform built for one question answers one question.

Published snapshot, Jan 2025 to Aug 2026. The refresh notebook regenerates the data up to yesterday, so its figures move, but the gap stays close to 20%.

Build log

  1. Now

    This storefront

    The website you’re on, built from the same synthetic dataset the series uses.

    Live
  2. Ep 1

    Foundation

    Store data, the generator and load notebook, Lakehouse, ontology and graph

    Building now
  3. Ep 2

    Two agents

    The semantic model, then the lakehouse agent against the ontology agent, on six real scenarios

    Planned
  4. Ep 3

    Live floor

    RFID readings streamed through an eventstream into an eventhouse, and bound to the ontology

    Planned
  5. Ep 4

    Watching agent

    One rule on floor stock, and an operations agent that asks in Teams

    Planned
  6. Ep 5

    Store app

    An app where managers work through replenishment tasks

    Planned
  7. Ep 6

    One assistant

    Copilot Studio, the ontology over MCP, and the returns policy index

    Planned
The dataset, its generator and the architecture notes are open: Yuktikara Store on GitHub. Want to see it from the shop floor instead? Find a store.