Transforming supply chain data into real-time intelligence with GenAI
Accuris built a GenAI-powered engineering assistant on the Databricks Data + AI Platform, using RAG, AI Search, MLflow and Model Serving to index over 40 million records across 1 billion+ parts, achieving a 10x increase in development velocity from concept to customer-facing feature.
Overview
Accuris built a GenAI-powered engineering assistant on the Databricks Data + AI Platform, using RAG, AI Search, MLflow and Model Serving to index over 40 million records across 1 billion+ parts, achieving a 10x increase in development velocity from concept to customer-facing feature.
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Inspect the highlighted sourceThe challenge
Accuris's catalog of electronic components, enriched with structured and unstructured parametric data, environmental attributes, pricing and documentation, was so voluminous and varied that generating AI-ready insights at scale was difficult; early traditional ML approaches required labor-intensive feature engineering, delivered inconsistent model performance, and demanded heavy compute and technical expertise, while sourcing teams needed faster, more accurate ways to identify alternate parts and reduce supply chain risk.
The solution
Accuris moved to the Databricks Data + AI Platform to build a GenAI-powered engineering assistant, using Unity Catalog to govern structured and unstructured data, Delta Lake and Spark Declarative Pipelines for reliable pipelines, managed MLflow for model development and tracking, and the Databricks suite of Agent Framework, AI Search and Model Serving for retrieval-augmented generation and real-time, domain-specific decision-making.
Reported business value
Accuris achieved a 10x increase in speed from concept to delivery, indexed over 40 million records for rapid semantic AI Search, and manages more than 1 billion parts across structured and unstructured data sources through its GenAI-powered engineering assistant, currently in early access with customers.
Sources
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