Building the Data Backbone for a $60M Supply Chain Platform
The platform connects brands with warehouses, ERPs, and POS systems — but the data coming in from 40+ supply chains was messy, inconsistent, and siloed. We built the integration engine that turned chaos into a unified, analytics-ready data ecosystem.
$60M+
Annual inventory managed on the platform
40+
Multi-layered supply chains integrated
3x
Faster queries after Redshift migration
Zero
Analyst backlog after NLQ assistant launch
The Company
The client operated a supply chain management platform that sits between brands and their distribution infrastructure — warehouses, ERPs, and point-of-sale systems. The platform tracks inventory, orders, and fulfillment across dozens of interconnected supply chains, each with its own data formats, naming conventions, and integration quirks.
With $60M+ in annual inventory flowing through the system and 40+ customer supply chains to manage, data quality wasn't a nice-to-have — it was the product. If the data was wrong, the inventory was wrong, and the supply chain broke.
The Challenge
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Dirty third-party data. Every warehouse, ERP, and POS system sent data in different formats with different naming conventions. The same product might appear as "Org Almnd Butter 16oz," "ALMOND BTR ORGANIC 1LB," and "Almond Butter - Organic (16 oz)" across three sources.
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Slow, brittle queries. The existing database couldn't handle the growing query volume. Peak-hour performance was degraded, and analysts waited minutes for basic reports.
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No integration health visibility. When an external integration broke or degraded, the team found out from customers — not from their own monitoring.
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Analyst bottleneck. Every ad-hoc question — "What's our fill rate for Brand X last quarter?" — required an analyst to write a query. Self-serve analytics didn't exist.
What We Built
Data Integration Engine
Built a purpose-designed integration engine that ingests, normalizes, and enriches third-party data from warehouses, ERPs, and POS systems. Automated schema monitoring within Spark pipelines catches upstream changes before they break downstream analytics. Implemented a medallion architecture (bronze → silver → gold) to create a high-reliability data foundation that accelerated client onboarding.
Graph-Based Product Catalog Normalization
This was the hard problem. User-entered product names across 40+ supply chains meant thousands of duplicates, misspellings, and abbreviation variants for the same products. We built a graph-based clustering system that identifies product relationships through string similarity, semantic matching, and transitive connections — consolidating the catalog into a unified, canonical product database. The result: dramatically improved demand forecasting accuracy, because the system finally knew that "Org Almnd Butter 16oz" and "ALMOND BTR ORGANIC 1LB" were the same product.
AWS Redshift Data Warehouse
Provisioned and migrated the analytics workload to AWS Redshift with proper VPC isolation, IAM policies, and Glue ETL pipelines. Query and fetch latency dropped to a third of what it was, and peak-hour performance stabilized. Infrastructure was built reproducibly — no snowflake servers, no manual provisioning.
Integration Health Dashboard
Shipped a proactive monitoring dashboard for all external integrations. Instead of learning about failures from customers, the team now sees integration health in real time — latency, error rates, schema drift, and data freshness — with alerts before problems reach production.
Natural Language Query Assistant
Built a RAG-based NLQ assistant using LangChain and Pinecone that turns plain-English questions into verified SQL queries. Added regression and trend tooling with verifiable arithmetic — eliminating hallucination risk for numerical answers. Analysts' ad-hoc question backlog disappeared; stakeholders could self-serve their own insights.
Technical Approach
Infrastructure
- AWS VPC, IAM, Redshift, Glue
- Spark ingestion pipelines
- Medallion architecture (bronze/silver/gold)
- Automated schema monitoring
AI & Analytics
- Graph-based clustering (product normalization)
- RAG + LangChain + Pinecone (NLQ)
- Regression & trend tooling (verifiable math)
- Integration health monitoring
The Impact
The platform now manages $60M+ in annual inventory with clean, unified data flowing from 40+ supply chains through a single reliable pipeline. Product catalog normalization improved demand forecasting. The Redshift migration made analytics fast enough to be useful. And the NLQ assistant turned a bottlenecked analyst queue into self-serve insights.
The integration engine became the foundation the product is built on — not a one-time project, but the ongoing data infrastructure that makes everything else possible.
What this means for your business
Dirty third-party data, slow queries, and analyst bottlenecks aren't unique to supply chain platforms. Any business that integrates data from multiple external sources — vendors, partners, SaaS tools — faces the same fundamental challenge: turning messy inputs into reliable outputs.
If your team is spending more time cleaning data than analyzing it, or if your integrations break silently, that's exactly the kind of problem we solve.
Talk to us about your dataServices used in this engagement:
This engagement was conducted by our principal consultant prior to founding Blue Ridge Dataworks.
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