Notesmith AI
Intelligent document summarization — not just shorter, actually better
The Problem
Summarization tools truncate. Notesmith understands.
Long documents and notes are hard to process quickly. Simple summarization — send everything to an LLM and ask it to shorten — breaks down on variable-length inputs and loses important structure. Notesmith uses an agentic pipeline where the LLM decides how to chunk, what to prioritize, and how to format output based on document type.
Architecture
How it's built
Agentic summarization pipeline with document-type awareness. The agent first analyzes document structure, then decides on a chunking strategy, then summarizes with awareness of what's important vs. filler. Output format adapts to content — bullet points for notes, narrative for articles, key facts for reports.
System Flow
Document Input
Raw text / file
Structure Analyzer
Type detection
Chunking Strategy
LLM decides splits
Priority Ranker
What matters most
Formatted Output
Type-aware summary
Document → Structure Analysis → Chunking → Priority Ranking → Formatted Summary
Tech Decisions
Why these choices
Agentic approach over a simple prompt
Handles variable-length input better. A single prompt breaks on long documents; an agent adapts its strategy to the content.
Document-type awareness
A meeting note and a research paper need different summaries. Detecting type first improves output quality significantly.
Challenges & Learnings
What was hard
- 01.
Handling edge cases in document length and structure — badly formatted inputs, code-heavy docs, multi-language content.
- 02.
Making the output genuinely useful, not just shorter. Shorter is easy. Useful requires judgment about what matters.
Screenshots / Demo
In action
Screenshot coming
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