Project Information
Azure Databricks
Synapse
Increaed Productivity
Reduction in cost associated with unnecessary staff
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TrueNorth partnered with a retail organization seeking to unlock deeper insights from unstructured product data.
The goal was to transform free-text product descriptions into structured, analyzable data that could be used to assess product overlap, cannibalization, and substitution trends across the portfolio.
The client’s vast inventory contained thousands of product entries with inconsistent or incomplete descriptions often recorded manually or through third-party imports.
This created several challenges:
The business needed a solution that could automatically extract and structure critical product details such as brand, measurement, and quantity from unstructured text fields at scale.
TrueNorth implemented an AI-powered text extraction framework using large language models (LLMs) to standardize and enrich product metadata.
Data Ingestion & Processing
Intelligent Text Parsing with LLMs
This information was validated and mapped into a structured data format suitable for analysis.
Integration & Visualization
Collect & Correlate
Application submission
Inspection
Release Letter
Premium Collection
Insurance Permit
The AI-driven data structuring pipeline allowed the client to transform fragmented text data into a reliable, searchable dataset. Significantly reducing manual analysis time and improving product-level decisioning.
By integrating LLMs into the retail analytics stack, TrueNorth enabled the business to move from raw text to intelligent product intelligence at scale.
Can this approach be applied beyond retail data?
How accurate is the extraction?
Does the model continue learning?
