Block 1 - Retrieve Parsed Content
- Type / Role
- n8n-nodes-base.httpRequest - httpRequest
- Config choices
- Version 4.2
Turn unstructured pitch decks and investment memos into polished Due Diligence PDF reports automatically. This n8n workflow handles everything from document ingestion to final delivery, combining i...
n8n-nodes-base.httprequest, n8n-nodes-base.webhook, n8n-nodes-base.code, n8n-nodes-base.splitinbatches, @n8n/n8n-nodes-langchain.vectorstorepinecone, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, n8n-nodes-base.aggregate
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Khairul Muhtadin.
Original n8n.io sourceTurn unstructured pitch decks and investment memos into polished Due Diligence PDF reports automatically. This n8n workflow handles everything from document ingestion to final delivery, combining internal document analysis with live web research to produce analyst-grade output in minutes.
Reviewing a single deal manually reading the deck, cross-checking claims online, formatting the summary easily takes half a day. Multiply that by 10–20 inbound deals per week, and your team is buried in low-leverage work before any real analysis begins.
This workflow compresses that cycle into a single automated pipeline.
Each deal gets a unique namespace in Pinecone, so documents are isolated and repeat uploads skip redundant parsing.
| Service | Role |
|---|---|
| n8n | Workflow orchestration |
| LlamaIndex Cloud | Document parsing (LlamaParse) |
| Pinecone | Vector storage & retrieval |
| OpenAI API | Embeddings (text-embedding-3-small) & LLM analysis (GPT-5.4) |
| Decodo API | Web search & page scraping |
| Cloudflare R2 | Report file storage (S3-compatible) |
| Symptom | Likely Fix |
|---|---|
| Parsing times out | Increase the Wait node duration; check file size against LlamaParse limits |
| Thin or generic analysis | Verify the source PDF is text-based, not a scanned image, enable OCR if needed |
| Broken PDF layout | Simplify CSS in the HTML render node; older Puppeteer builds handle basic layouts better |
Created by: Khmuhtadin Category: Business Intelligence | Tags: AI, RAG, Due Diligence, Decodo
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 61 workflow blocks. Download the JSON for the full node graph.
| Workflow | Generate M&A due diligence reports with Decodo, OpenAI and Pinecone |
|---|---|
| Complexity | advanced |
| Nodes | 61 |
| Categories | Document Extraction, AI RAG |
| Author | Khairul Muhtadin |
| Published | 18 Feb 2026 |
Use the JSON export at /data/workflows/13500/13500.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Turn unstructured pitch decks and investment memos into polished Due Diligence PDF reports automatically. This n8n workflow handles everything from document ingestion to final delivery, combining i...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI RAG use case.