Generate complete presentation decks directly from your knowledge base.

Upload PDFs, documents, notes, or other resources, then ask a question or provide a topic. The system researches the available context, creates a structured narrative, generates slide content, suggests visuals, and produces a presentation-ready deck.

Inspired by modern AI research assistants, but built around retrieval, reasoning, and presentation generation workflows.

Traditional RAG systems answer questions.

This project goes a step further.

Instead of returning a single response, it transforms retrieved knowledge into a structured presentation with:

• Research-backed content
• Logical slide flow
• Speaker notes
• Visual recommendations
• Presentation-ready output

The goal is to convert large collections of documents into educational, business, technical, or research presentations automatically.

The system accepts resources such as:

• PDFs
• Research papers
• Technical documentation
• Notes
• Articles
• Internal knowledge bases

Resources are parsed, chunked, embedded, and stored for retrieval.

When a user requests a presentation, the system retrieves the most relevant information from the knowledge base.

Example:

Create a presentation on Retrieval Augmented Generation

Relevant chunks are collected and merged into a research context.

The retrieved information is transformed into structured research notes.

Research includes:

• Key themes
• Important concepts
• Supporting facts
• Definitions
• Insights
• Relationships between topics

This stage creates a unified understanding of the subject before slide generation begins.

The system generates a presentation outline.

Example:

1. Introduction
2. Problems with Traditional Search
3. What is RAG?
4. Vector Databases
5. Retrieval Pipelines
6. Hybrid Search
7. Agentic RAG
8. Future Directions
9. Key Takeaways
10. Conclusion

The outline becomes the backbone of the deck.

Each slide is generated independently while maintaining consistency with the overall presentation structure.

Each slide may contain:

• Title
• Subtitle
• Bullet points
• Explanations
• Speaker notes
• References

For every slide, the system determines the most appropriate visual representation.

Supported visual types:

• Diagrams
• Flowcharts
• Timelines
• Tables
• Architecture diagrams
• Comparison charts
• Process visualizations

The final deck can be exported into formats such as:

• PPTX
• HTML Slides
• Reveal.js Presentations
• JSON Presentation Schema

Resources
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Ingestion Pipeline
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Vector Database
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Retriever
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Research Generator
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Outline Generator
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Slide Generator
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Visual Planner
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Presentation Renderer

Responsible for:

• Document parsing
• Chunking
• Metadata extraction
• Embedding generation
• Indexing

Responsible for:

• Semantic search
• Context gathering
• Relevance ranking
• Source selection

Responsible for:

• Knowledge synthesis
• Theme extraction
• Fact aggregation
• Topic understanding

Responsible for:

• Slide ordering
• Storytelling flow
• Presentation structure
• Content hierarchy

Responsible for:

• Slide creation
• Bullet generation
• Notes generation
• Content formatting

Responsible for:

• Diagram suggestions
• Timeline generation
• Chart recommendations
• Architecture visualizations

Upload PDFs
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Ask:
"Generate a presentation on Graph RAG"
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Retrieve Relevant Context
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Create Research Notes
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Generate Outline
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Generate Slides
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Suggest Visuals
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Export Presentation

• Multi-document presentations
• Web search integration
• Citation-aware slides
• Interactive presentations
• Voice narration generation
• AI-generated diagrams
• Collaborative editing
• Presentation theme customization
• Automatic chart generation
• Multi-language support

The objective is to evolve beyond question-answering systems and build an AI-powered presentation engine capable of transforming large knowledge repositories into structured, visually rich, presentation-ready decks with minimal human effort.