Introduction

In today’s digital-first environments, a large portion of enterprise knowledge lives inside video content, training sessions, onboarding walkthroughs, and recorded operational procedures.

While videos are great for learning, they are not ideal for quick reference, compliance, or repeatable processes. Converting that knowledge into structured documentation like Standard Operating Procedures (SOPs) is often manual and time-consuming.

What if this process could be automated using AI?

The Problem

Transcripts alone don’t solve the problem.

When videos are converted into text, the output typically lacks:

• Clear structure (sections, headings, hierarchy)
• Context (relationships between steps, tools, and roles)
• Completeness (definitions and dependencies spread across the content)

This leads to a common challenge:

Teams spend significant effort manually reading transcripts, interpreting context, and restructuring them into usable documentation.

As seen in modern architecture challenges, manual and repetitive configurations don’t scale well and increase maintenance effort 

Enter Graph-based RAG (GraphRAG)

GraphRAG extends traditional RAG by building a knowledge graph instead of treating content as disconnected chunks.

What GraphRAG Does

• Extracts entities (tools, systems, roles, concepts)
• Maps relationships between them
• Groups related concepts into logical sections
• Preserves context across the entire document

Architecture Overview

Below is the high-level pipeline:

Video → Transcription → Knowledge Graph → LLM Generation → Structured SOP

Implementation Approach (Step-by-Step)

Stage 1: Knowledge Graph Construction

1. Convert video to transcript
2. Split transcript into chunks
3. Feed chunks into GraphRAG

GraphRAG performs:

• Text Unit Extraction
• Entity Recognition
• Relationship Mapping
• Community Detection

Result: A structured knowledge graph representation of the transcript

Stage 2: Structure Extraction

From the knowledge graph:

Sequential Steps

• Preserve procedural flow from transcript order

Logical Sections

• Derived using community detection

Key Concepts

• Identified using graph centrality (importance via connections)

This creates a framework for the SOP

Stage 3: Intelligent Document Generation

Using Azure OpenAI, each SOP section is generated:

The key advantage: LLM is grounded in graph structure not raw text

Key Benefits

• Context Preservation - Relationships between concepts are maintained across sections.

• Comprehensive Coverage - Community detection ensures important topics are not missed.

• Reduced Hallucination - LLM generation is grounded in structured knowledge.

• Scalability- Works for:  30-minute tutorials, 3-hour training sessions and Enterprise knowledge bases

Real-World Impact (Example)

In enterprise scenarios like pharmaceutical SOP generation:

• Processing time: ~15–20 minutes for a multi-hour video
• Output quality: 8–10 structured SOP sections
• Consistency: Terminology and relationships preserved
• Coverage: Minimal missing topics

Where This Approach Works Best

• Training videos → SOPs
• Meeting recordings → action summaries
• Technical demos → documentation
• Interview recordings → knowledge bases
• Tutorials → reference guides

Key Takeaway

This approach represents a shift from text processing → knowledge understanding.

By combining:

• Knowledge graphs (structure)
• LLMs (language generation)

We can transform raw, unstructured content into usable, enterprise-grade knowledge assets.

Resources

• https://microsoft.github.io/graphrag/index/overview/

Final Thoughts

Have you explored GraphRAG or similar approaches in your projects?

• What challenges did you face?
• How did you handle unstructured knowledge?

Share your experiences — let’s learn together.