Build Document Intelligence Using Connectors.
You have 500 PDFs, contracts and reports that contain critical business knowledge locked inside files nobody reads , Unstructured extracts the content, Pinecone makes it searchable, and Notion indexes every document
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your agent receives 500 documents: contracts, technical reports, financial statements, meeting notes , a mix of PDFs, Word docs, presentations and scanned images.
Step 1: Unstructured processes every document regardless of format. PDFs get OCR for scanned pages. Tables are extracted as structured data.
Headers create section boundaries. Images get descriptions. Step 2: Pinecone stores the processed content as vector embeddings. Now you can search: 'Find all contract clauses about liability limitations' returns relevant paragraphs from 47 contracts , across PDF, Word and scanned documents.
Step 3: Notion maintains the document registry: 'Contract_2024_ClientA.pdf , Processed June 4. 34 pages, 127 chunks, 14 tables extracted. Search status: Indexed in Pinecone.
Key clauses: liability (7), termination (12), IP ownership (15).'
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Unstructured, Pinecone and Notion so your AI agent processes documents of any format , PDFs, Word docs, spreadsheets, presentations , using Unstructured's document processing pipeline, stores extracted content as searchable vector embeddings in Pinecone, and maintains a document registry in Notion.
Unstructured
triggerProcesses any document format , PDFs, Word, PPTX, images , into clean structured content ready for embedding
list_processing_workflows trigger_workflow_execution get_workflow_details list_workflow_jobs Pinecone
enrichmentStores processed document content as searchable vector embeddings for instant semantic retrieval
query_vectors describe_index get_index_stats fetch_vectors Notion
actionDocument registry with processing status, metadata, and searchable catalog
create_page query_database search_pages get_page Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Unstructured, Pinecone & Notion ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
Legal teams making 500 contracts searchable by clause type without reading every document
AI builders creating RAG knowledge bases from mixed-format document libraries
Research teams processing technical reports into searchable vector databases
AI enthusiasts building personal document intelligence systems from their file archives
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need?
Three: Unstructured, Pinecone and Notion.
Does this work with Claude Desktop?
Yes. Any MCP-compatible AI client works.
What document formats are supported?
PDF, Word, PowerPoint, Excel, HTML, images (with OCR), and more. Unstructured handles format complexity.
Is my document data secure?
Connectors authenticate via API keys. Documents are processed via Unstructured's API. Data stays in your accounts.
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Connectors used in this workflow
Unstructured
Unstructured MCP for AI Agents. This Connector lets you manage data ingestion pipelines, trigger document processing jobs, and monitor RAG data workflows directly from your AI client. It connects your agent to Unstructured.io so you can handle messy file transformations without leaving your editor.
Pinecone
Pinecone MCP lets your AI agent talk directly to your vector database. Query embeddings, manage collections, and pull performance stats through natural conversation. It turns your vector store into an accessible knowledge base for your agent without you having to write custom scripts.
Notion
Notion MCP lets your AI agent read, search, and update your Notion workspace. It turns static docs into a live database your agent can query to find project specs, manage tasks, and update team pages instantly. This makes your company knowledge searchable and actionable from any MCP-compatible client.