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How to Build an AI Document Summarizer SaaS (MVP Architecture)
MT
MVPHub Team
•
2 min read
How to Build an AI Document Summarizer SaaS (MVP Architecture)
An AI document summarizer is one of the most practical AI SaaS products. Users upload documents (PDFs, articles, papers), and your tool generates concise summaries. Here's the architecture.
The Architecture
User uploads document
→ File storage (S3/Cloudinary)
→ Text extraction (PDF parser)
→ Chunking (split into manageable pieces)
→ Summarization (LLM call per chunk, then combine)
→ Display summary + save to database
Step 1: File Upload and Storage
Supported formats: PDF, DOCX, TXT, URLs (web scraping) Storage: AWS S3, Cloudinary, or Uploadthing File size limit: 10-50MB for MVP
Step 2: Text Extraction
| Format | Tool |
|---|---|
pdf-parse (Node.js) or PyPDF2 (Python) | |
| DOCX | mammoth (Node.js) |
| Web pages | cheerio or puppeteer for scraping |
| Plain text | Direct read |
Step 3: Chunking Strategy
LLMs have token limits. Long documents must be split into chunks.
Approach: Split by paragraphs or sections, keeping each chunk under 4,000 tokens. Overlap chunks by 200 tokens to maintain context.
Step 4: Summarization
Map-Reduce approach:
- Map: Summarize each chunk independently
- Reduce: Combine chunk summaries into a final summary
This handles documents of any length while staying within token limits.
Step 5: Output
- Executive summary — 2-3 paragraph overview
- Key points — Bullet list of main takeaways
- Chapter/section summaries — For long documents
4-Week Build Plan
| Week | Focus |
|---|---|
| 1 | SaaS boilerplate setup, file upload, text extraction |
| 2 | Chunking + summarization pipeline, output display |
| 3 | Credit system, usage tracking, Stripe billing |
| 4 | Polish, landing page, deploy, launch |
Need a SaaS boilerplate to start? Browse on MVPHub.
More AI ideas: Read AI MVP Ideas You Can Build.
