AI/MLAzure OpenAIRAGLLMsVector SearchGPT-4Knowledge ManagementAutomation

TL;DR

A global enterprise struggled with inconsistent documentation, slow internal support, and knowledge trapped in PDFs, Confluence pages, SharePoint, and tribal knowledge.

I designed and delivered a secure, private AI knowledge assistant using Azure OpenAI, RAG pipelines, and vector search — enabling employees to get instant, accurate answers grounded in the company's own data.

The result: faster support, reduced operational overhead, and an AI foundation ready for future automation.

AI Knowledge Assistant Interface

AI Case Study: Intelligent Knowledge Assistant with RAG & Azure OpenAI

Transforming fragmented documentation into a secure, enterprise-grade chat assistant

70%
Faster Support
60%
Self-Service Increase
40%
Support Load Reduction
90 Days
Multi-Dept Adoption

Impact Summary

  • Response times for internal support reduced by ~70%
  • Employee self-service increased by ~60% after rollout
  • Operational load on support teams decreased by ~40%
  • Knowledge accuracy improved through centralized sources + retrieval grounding
  • AI assistant adopted across multiple departments within first 90 days

Client Context

The client had thousands of documents spread across:

  • • SharePoint
  • • Confluence
  • • Network file shares
  • • Email attachments
  • • Product manuals and policy PDFs

Employees struggled to find information quickly, resulting in:

  • • Slow onboarding
  • • Repeated questions to support/IT teams
  • • Inconsistent answers across departments
  • • No single "source of truth"

The business needed an AI-powered internal assistant that could:

  • • Understand natural-language questions
  • • Retrieve information from company-approved content
  • • Provide accurate, safe, traceable answers
  • • Be deployed securely inside their Azure tenant

The Challenge

Technical Problems

  • Scattered documentation with different formats (PDF, Word, HTML, JSON)
  • No unified indexing strategy
  • Different document versions with outdated content
  • Hallucination risk with naive LLM usage
  • Strict security/compliance requirements (EU data sovereignty)

Organizational Problems

  • No central knowledge governance team
  • Fear of exposing sensitive content
  • Concerns around AI adoption and trust
  • Need for clear content owners and lifecycle

The solution needed to be accurate, secure, compliant, and easy for employees to adopt.

AI RAG Architecture with Azure OpenAI

My Role & Approach

As the AI Architect, I led the entire initiative — from architecture and security design to model prompt engineering, RAG pipelines, and integration with internal systems.

1. Knowledge Assessment & Data Strategy

I conducted a full audit of:

  • Document locations
  • Formats and update frequency
  • Existing classification
  • Business-critical content
  • Access levels per Business Unit

Output: A curated, approved, deduplicated knowledge corpus for AI indexing.

2. RAG Architecture & Vectorization Pipeline

I designed a production-grade Retrieval-Augmented Generation (RAG) pipeline using:

Azure Services

  • • Azure OpenAI (GPT-4/4o models)
  • • Azure Cognitive Search or Pinecone vector DB
  • • Azure Blob Storage / SharePoint connectors
  • • Azure Functions for ingestion
  • • Azure API Management for secure access
  • • Azure Key Vault + Managed Identities
  • • Application Insights for monitoring

Pipeline Highlights

  • • Chunking & embedding of thousands of pages
  • • Semantic search with vector-based retrieval
  • • Secure content filtering based on RBAC
  • • Source citations for every answer (no "black box")
  • • Hallucination prevention through guardrails & system prompts

This ensured answers were accurate, grounded in approved data, and traceable.

3. Building the AI Assistant (ChatAgent)

I built the assistant with:

  • Multi-turn conversation memory
  • Structured tool calling to access APIs (e.g., HR or IT data)
  • Context windows optimized for enterprise docs
  • Role-based behavior (HR mode, IT mode, Engineering mode)
  • Advanced prompts for safe-answer guidelines

The assistant provided:

  • • Document answers
  • • Policy summaries
  • • Step-by-step task guidance
  • • Links to original documents
  • • Escalation suggestions when needed

4. Security, Compliance & Governance

To comply with the client's security requirements:

  • No training on customer data (RAG-only)
  • All embeddings stored in private vector DB
  • Full data residency inside EU region
  • Audit logging through App Insights
  • Content-access rules matched corporate RBAC

I also helped the company set up a Knowledge Governance Model, including:

  • • Content Owners
  • • Review Cadence
  • • Version control
  • • Approval workflows

5. Integration with Existing Platforms

I integrated the assistant into:

  • Microsoft Teams (primary interface)
  • Intranet Portal (custom web app)
  • ServiceNow (for IT support flows)

Teams adoption skyrocketed because users could ask questions naturally in a familiar interface.

The Outcome

After go-live, the organization gained:

A private, enterprise-grade ChatGPT tailored to their own knowledge
Faster onboarding and reduced dependency on senior staff
Drastically fewer repetitive questions to support desks
Standardized answers across all Business Units
A foundation to expand into AI workflows, automations, and agentic tasks
Improved employee productivity and satisfaction

Employees embraced the assistant quickly because it was accurate, helpful, and grounded in their language and documentation.

Why This Matters for Future Clients

I help organizations:

  • Build secure, private AI assistants that accelerate internal productivity
  • Turn unstructured documents into a searchable knowledge system
  • Implement RAG pipelines and vector search architectures
  • Ensure governance, accuracy, and compliance
  • Integrate AI agents into the business workflows they already use
  • Create scalable foundations for future agentic automation

If you're exploring AI or want to offer your teams a secure, intelligent knowledge assistant, I can lead the full transformation from idea to production.

This project demonstrates how AI can transform internal knowledge management when implemented with proper architecture, security, and governance—delivering measurable productivity gains while maintaining enterprise compliance and trust.

Client Feedback

"Iulian delivered exactly what we needed—a secure AI assistant that our employees actually trust and use. The RAG architecture he designed ensures answers are grounded in our documentation, not hallucinated. We've seen a dramatic reduction in support tickets and our onboarding process is now significantly faster. More importantly, he built this with security and compliance at the core, which was non-negotiable for us. This is our foundation for AI-driven automation going forward."
Chief Technology Officer
Global Enterprise Client

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