Secure RAG Knowledge Copilot Case Study | Prismetric

Building a Secure RAG-Powered Enterprise Knowledge Copilot

How we transformed fragmented enterprise knowledge into a secure copilot that delivers accurate, permission-aware answers with source citations.

Our RAG-Powered SolutionDiscuss Your Project

68%

Faster Knowledge Retrieval

61%

Queries Resolved Independently

89%

Retrieval Precision

350+

Hours Saved Monthly

Project at a Glance

Client Profile Business Challenge Solution
Multinational Enterprise Fragmented Knowledge Access Secure RAG Knowledge Copilot
Primary Users Connected Sources Access & Security
Employees Across Departments SharePoint, Drive & PDFs SSO, RBAC & Source Permissions

Client Overview

The client is a multinational enterprise with teams operating across multiple departments, regions, and business functions. Employees regularly relied on SOPs, HR policies, technical manuals, project documents, and internal guidelines to complete daily tasks and make informed decisions.

However, this critical knowledge was scattered across SharePoint, Google Drive, PDF repositories, and internal systems. Employees often spent valuable time searching through multiple platforms or contacting support teams to locate accurate and up-to-date information.

To improve enterprise knowledge access, the organization partnered with Prismetric to build a secure RAG-powered knowledge copilot. The solution enables employees to ask questions in natural language and receive accurate, permission-aware answers supported by citations from approved company documents.

Challenges in Enterprise Knowledge Access

Enterprise information was scattered across multiple systems, making reliable knowledge difficult to find. Here’s what employees were up against:

Fragmented Knowledge Sources

Critical documents were distributed across SharePoint, Drive, PDFs, and internal portals.

Slow Information Retrieval

Employees spent valuable time searching documents or contacting internal support teams.

Outdated and Inconsistent Content

Multiple document versions made it difficult to identify the latest approved information.

Complex Access Controls

Sensitive content required strict role-based and document-level permissions.

The client needed a knowledge solution that was always accurate, always accessible, and secure for every employee.

Our RAG-Powered Solution

We built a secure enterprise knowledge copilot that retrieves approved information, respects user permissions, and delivers accurate answers with source citations.

Unified Knowledge Ingestion

  • Connected SharePoint, Google Drive, PDFs, and internal repositories.
  • Cleaned, organized, and processed documents for reliable retrieval.
  • Created department-specific knowledge collections for faster access.

Intelligent Search and Answers

  • Combined semantic and keyword search for better results.
  • Retrieved the most relevant document sections for each query.
  • Generated contextual answers with clear source citations.

Secure Access and Optimization

  • Applied SSO, role-based access, and document permissions.
  • Monitored unanswered questions and retrieval performance.
  • mproved response quality using employee feedback and analytics.

How the RAG Knowledge Copilot Works

The system transforms enterprise content into secure, searchable knowledge and delivers reliable answers through a structured retrieval process.

Connect Knowledge Sources

Connect Knowledge Sources

The platform securely connects with SharePoint, Google Drive, PDFs, and internal repositories.

Process Enterprise Documents

Process Enterprise Documents

Documents are extracted, cleaned, divided into sections, and enriched with relevant metadata.

Build a Searchable Index

Build a Searchable Index

Content is converted into embeddings and stored within a secure vector database.

Retrieve Relevant Context

Retrieve Relevant Context

Hybrid search identifies the most useful document sections for each employee query.

Generate Grounded Answers

Generate Grounded Answers

The language model creates responses using only the retrieved and authorized information.

Cite Sources and Improve

Cite Sources and Improve

Every answer includes source references, while feedback helps improve future retrieval accuracy.

Core Features and Integrations

The knowledge copilot combined secure enterprise integrations with intelligent retrieval features to deliver reliable information across departments.

Multi-Source Integration

Multi-Source Integration

Connected SharePoint, Google Drive, PDFs, and approved internal knowledge repositories.

Permission-Aware Retrieval

Permission-Aware Retrieval

Displayed information according to each employee’s role, department, and document access rights.

Source-Backed Conversations

Source-Backed Conversations

Provided clear citations, document links, and conversation history with every relevant answer.

Admin Analytics Dashboard

Admin Analytics Dashboard

Tracked usage, unanswered queries, employee feedback, and retrieval performance from one dashboard.

Enterprise Security and AI Quality Controls

The solution was designed to protect sensitive enterprise knowledge while ensuring every response remained accurate, traceable, and permission-aware.

SSO and Role-Based Access

SSO and Role-Based Access

Employees accessed the copilot through existing credentials and received information based on assigned roles.

Document-Level Permissions

Document-Level Permissions

The system verified source permissions before retrieving or using content in generated answers.

Grounded Answers and Citations

Grounded Answers and Citations

Responses were generated from approved documents and supported with clear source references.

Confidence and Escalation Controls

Confidence and Escalation Controls

Low-confidence or unsupported queries were redirected to the appropriate internal team.

Our Implementation Approach

We followed a structured development process to ensure the knowledge copilot was secure, accurate, scalable, and aligned with employee workflows.

Discovery and Assessment

Discovery and Assessment

We reviewed user needs, knowledge sources, access rules, and frequently requested information.

Data Preparation

Data Preparation

Documents were cleaned, classified, structured, and prepared for secure enterprise retrieval.

Development and Integration

Development and Integration

We built the RAG pipeline, user interface, source integrations, and access controls.

Testing and Optimization

Testing and Optimization

The system was evaluated for retrieval accuracy, response quality, security, and performance before deployment.

The Results

The enterprise knowledge copilot improved how employees searched, verified, and used business information across departments.

Faster Knowledge Access

Faster Knowledge Access

Employees found relevant information without searching across multiple platforms or document repositories.

Fewer Support Escalations

Fewer Support Escalations

Common HR, IT, policy, and process questions were resolved without internal team assistance.

More Consistent Answers

More Consistent Answers

Source-backed responses helped employees follow the latest approved policies, procedures, and guidelines.

Better Knowledge Visibility

Better Knowledge Visibility

Analytics revealed popular topics, unanswered queries, outdated content, and gaps in the knowledge base.

Business Impact

The RAG-powered knowledge copilot created measurable improvements in employee productivity, support efficiency, and enterprise knowledge governance.

Employee Productivity

Employee Productivity

Employees spent less time searching for information and more time completing high-value business tasks.

Support Efficiency

Support Efficiency

HR, IT, and operations teams handled fewer repetitive questions and focused on complex employee requests.

Knowledge Governance

Knowledge Governance

Approved sources, access controls, and citations created a more reliable and controlled knowledge environment.

68%

Faster Knowledge Retrieval

61%

Queries Resolved Independently

89%

Retrieval Precision

350+

Hours Saved Monthly

Technology Used

Technology Behind the Enterprise Knowledge Copilot

To build this Rag-powered enterprise knowledge copilot, we integrated:

Azure OpenAI

Generated contextual, source-grounded answers from retrieved enterprise information.

OpenAI Embeddings

Converted document content into vector representations for semantic search.

PostgreSQL with pgvector

Stored document embeddings, metadata, and searchable knowledge indexes.

Python and FastAPI

Powered document ingestion, retrieval workflows, authentication, and enterprise integrations.

Microsoft Azure

Provided secure cloud deployment, monitoring, logging, and scalable infrastructure. For a conceptual or anonymized case study, use:

Large Language Model

Generated contextual answers using retrieved and authorized enterprise information.

Embedding Model

Converted documents into vector representations for semantic retrieval.

Vector Database

Stored embeddings and metadata for fast, scalable knowledge search.

Backend Framework

Managed ingestion, retrieval, authentication, analytics, and system integrations.

Cloud Infrastructure

Supported secure deployment, monitoring, logging, and performance scaling.

Client Testimonial

The knowledge copilot gave our employees a faster and more reliable way to access company information while maintaining the security and permissions required across departments.

Head of Digital Transformation, Paul Rode

Multinational Enterprise

Transforming Enterprise Knowledge with Secure RAG

The enterprise knowledge copilot unified scattered information into a secure, intelligent, and easily accessible system. By combining permission-aware retrieval, source-backed answers, and continuous performance monitoring, Prismetric helped employees find reliable information faster while reducing pressure on internal support teams. The solution created a scalable foundation for improving knowledge access, governance, and productivity across the organization.

Want to Build a Secure Enterprise Knowledge Copilot?

Turn scattered enterprise documents into accurate, permission-aware, and source-backed answers with a custom RAG solution built around your business needs.

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