Knowledge Retrieval – TrueNorth Group

Project Information

Client Name: HealthCare

Techniques: Woe LR

Technology: Power Bi
Azure Databricks
Synapse

Business Benefits Improved Patient Care
Increaed Productivity
Reduction in cost associated with unnecessary staff

Industry:

Retail

Techniques:

GPT-powered LLMs, Retrieval-Augmented Generation (RAG)

Technology Stack:

Power BI, Azure Databricks, Synapse Analytics

Business Benefits:

Faster retrieval of information from unstructured documents

Improved customer experience

Access to SME information for new staff

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Overview

TrueNorth partnered with a retail enterprise to unlock the value hidden within unstructured business documents such as product manuals, emails, and support tickets.

By deploying a fine-tuned Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) architecture, the team built an AI-powered knowledge retrieval system that provides instant, accurate responses to both employees and customers.

The Challenge

The client managed a growing library of documents — product specifications, support logs, and supplier manuals — but struggled with:

Slow and inconsistent search across fragmented systems

Repetitive customer queries that agents had to handle manually

Loss of institutional knowledge as new employees joined

TrueNorth needed to consolidate these disparate information sources into an intelligent retrieval framework that would improve internal efficiency and customer experience.

Our Approach

TrueNorth developed an LLM-driven knowledge assistant using GPT-based models and secure data pipelines to process, catalog, and query unstructured information.

Data Fusion & Cataloguing

Collected and indexed unstructured text (e.g., manuals, CRM logs, support emails) into a unified knowledge repository for fast retrieval.

Fine-Tuned LLM Integration

Used Azure OpenAI and RAG fine-tuning to create a context-aware AI model capable of retrieving accurate information based on user intent.

CRM Integration & Feedback Loop

Integrated the AI assistant directly into the client’s CRM platform, enabling automated query resolution and learning from each customer interaction to improve future accuracy.

Collect & Correlate

Collected and structured key datasets, including:

Demographics and population health data

Epidemiology and diagnosis statistics

Historical patient volumes and level-of-care requirements