CASE STUDY DETAILS

AI & Sales Analytics Platform

Project Type

Analytics

Challenge

Fragmentation

Domain

Retail

Impact

Visibility

OVERVIEW

The project is an automated document processing and enterprise data management platform that combines Python-based document extraction, and Snowflake Cloud Data Warehouse.

The platform automates the complete journey of business documents—from PDF/image ingestion and information extraction to data validation, transformation, centralized storage, and analytics.

It is designed to reduce manual document processing, improve data quality, and provide a reliable centralized data layer for downstream reporting and business operations.

PROBLEM STATEMENT

The existing/manual approach created several operational challenges.

The business process involved handling a large number of invoices/documents that could contain both machine-readable text and scanned images.

  • Employees had to manually extract information from documents.
  • Data Quality Challenges
  • Data Management Challenges
  • Raw document information was not immediately available in a centralised analytical platform

SOLUTIONS

It is designed to reduce manual document processing, improve data quality, and provide a reliable centralized data layer for downstream reporting and business operations.

1. Layered Snowflake Data Architecture

  • A four-layer pipeline (Raw → Staging → Analytics → Marts) that separates landing, cleaning, modeling, and business consumption — so every layer has one clear job and issues are traceable to their source.

3. Kimball-Style Star Schema

  • Snowflake Streams and Tasks automatically detect and propagate only new or changed data, keeping the platform current without manual reprocessing or full-table reloads.

5. Governed Data Quality Framework

  • Automated checks for nulls, referential integrity, duplicates, and invalid values run after every load and are logged centrally

2. Automated Incremental Processing

  • Snowflake Streams and Tasks automatically detect and propagate only new or changed data, keeping the platform current without manual reprocessing or full-table reloads

4. Kimball-Style Star Schema

  • Conformed dimensions (customers, products, campaigns, date) and fact tables (order items, returns, support, campaign events) give the business a consistent, query-friendly model that BI tools and analysts can trust.
Sales analytics

APPROACH

Our Approach

Architecture-First Design

  • We started by mapping the four business questions directly to a target data model, not the other way around.
  • This ensured every table and pipeline stage existed to serve a real business answer, not just to store data.

CLI-Driven, Version-Controlled Development

  • All environment setup, transformations, and security policies were scripted and run through the Snowflake CLI.
  • This made the entire build repeatable, auditable, and ready to hand off to an engineering team via Git.

Incremental, Layer-by-Layer Build

  • We built and validated one layer at a time — raw ingestion, then staging, then the star schema, then marts.
  • Each layer was checkpointed with row-count and integrity validation before moving to the next, catching issues early.

Automation Over Manual Refresh

  • Streams and a Task DAG were introduced early so the pipeline runs on its own schedule, not on-demand.
  • This reflects how the platform will actually operate in production, not just how it looks in a one-time demo.

Validate, Then Visualize

  • Every mart was checked against its source facts before a single dashboard tile was built.
  • This meant the three business dashboards were built on already-trusted numbers, not numbers that looked plausible.
SUCCESS AND IMPACT

What Was Achieved

  • product, customer, campaign, and financial-leakage data now live in one governed platform instead of scattered spreadsheets and siloed reports
  • Product performance made visible
  • Customer value quantified

By Business

  • Business Documents
  • Snowflake Staging
  • Data Quality Validation
  • Reporting & Analytics
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