Agentic Ai in Action:
The Instant Document Finder

The Goal/Use Case

To let your team ask natural language questions (like "What is our warranty policy?") and get accurate answers instantly from hundreds of PDF manuals, reports, or legal documents.

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The Challenge:

Critical business knowledge is often locked inside PDF files, making it impossible to search easily. Finding an exact detail in a 50-page document is slow, and team members often give up, leading to wrong answers or delays in customer service.

Bookshelves filled with numerous books behind a table with more books stacked on top.

The Agentic Ai Solution:

This system creates a smart, searchable library of your documents. It uses Ai to read every page, remember the content, and decide which exact sentence is the perfect answer to a team member's question. It's like having a document expert available 24/7.

The 4 Steps of this Ai Workflow

The Instant Document Finder

Step Business Function What It Does (The Benefit)
1. The Digital Scanner PDF Processing & Text Extraction Documents are automatically uploaded and scanned. The system uses a special AI (Mistral) to read the text from images and pages, pulling out every single word. It converts pictures into searchable text.
2. The Smart Filer Deep Storage and Indexing The extracted text is turned into a "smart memory" file. These files are stored in a specialized database where the system can instantly find things based on meaning, not just keywords. It stores knowledge for instant recall.
3. The Answer Engine Question-Answering with RAG When someone asks a question, the system finds the relevant text chunks from the database and gives them to a powerful AI (Gemini). This AI then writes a clear answer that is guaranteed to be based only on the facts in your PDFs.
4. The Optimizer (Optional) Performance and Size Control You can choose a "light mode" where the AI summarizes each page before storing it. This makes the system faster and cheaper for documents that don't need every detail saved. It helps control costs and speed.
Screenshot of a digital workflow diagram for a PDF document OCR system with Mistral OCR, Qdrant, and Gemini AI, featuring colored sections and various process icons.
Flowchart showing steps for creating a document collection using Qdrant and Google Drive with nodes labeled 'Create collection' and 'Refresh collection'.
Workflow diagram showing steps for PDF processing and OCR, including getting a PDF, uploading to Mistral, signing URL, and OCR, with icons for workflow triggers, Google Drive, web, and document scanning.
Screenshot of a workflow diagram for an OCR system with various steps including OCR OCR, code processing, looping over items, and setting pages, with blocks for summarization and embeddings.
Flowchart showing steps for processing and summarizing a document using various AI and data tools, including model setup, looping through items, setting pages, and embedding content, with categories like code, data loader, wait, and splitters.
Flowchart of a chatbot system with steps for processing messages, retrieving information from Google Gemini and Vector Store, and testing the RAG system.
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The Evidence

  • Automation: Documents are processed and made queryable automatically—no manual effort is required.

  • Accuracy: Answers are grounded in your actual documents, reducing mistakes and improving internal consistency.

  • Technology: Built with latest Ai technology, combining the best tools (Mistral, OpenAI, Gemini) to give you powerful, instant search over your data.

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