Enterprises & Operations Teams

    Document Automation System

    An end-to-end automation that extracts structured intelligence from documents received via UI, Email, or WhatsApp and stores it cleanly in MongoDB.

    Problem Statement

    Businesses receive documents from everywhere: Upload forms, Email attachments, WhatsApp files, PDFs, scans, images, and mixed formats. Manually processing these documents means opening files one by one, reading long documents, copying data into systems, missing important fields, inconsistent tagging, no centralized storage, and zero scalability. Teams needed a way to automatically understand documents, extract what matters, and store everything in a structured, searchable format — without human review.

    The Solution

    Automation Overview

    We built a complete document automation pipeline that accepts files from multiple channels (UI, Email, WhatsApp), analyzes them with AI, extracts structured data, and stores the results in MongoDB. The system doesn’t just read text — it understands document context, structure, and intent.

    Multi-Channel Document Intake

    The workflow accepts documents from Web UI uploads, Email attachments, and WhatsApp messages. All inputs are normalized into a single processing pipeline, regardless of where the document comes from.

    AI-Powered Document Understanding

    Using a Google Gemini–powered AI agent, the system analyzes each document and extracts title, department, summary, key points, keywords, and business labels. The AI understands context instead of relying on rigid templates.

    Physical & Structural Element Detection

    Beyond plain text, the workflow detects tables, signatures, layout indicators, multi-page structures, and embedded sections. This allows the system to differentiate between content types like invoices, contracts, reports, or internal documents.

    Language & Classification Detection

    The automation automatically detects the document language, applies internal business labels, classifies documents by purpose or department, and normalizes outputs for consistent storage.

    Clean Structured Output (Core Intelligence)

    All extracted data is converted into a clean JSON structure that includes metadata, extracted fields, AI-generated summary, detected elements, and confidence indicators. No raw blobs. No messy text.

    MongoDB Storage & Indexing

    The final structured output is stored in MongoDB, making it searchable, filterable, and ready for dashboards, RAG systems, or analytics. This creates a reliable document intelligence database.

    Error Handling & Reliability

    The system includes file-type validation, empty-content detection, AI fallback handling, safe retries, and logging for failed documents. Documents never silently fail.

    Integrations & Connected Systems

    UI Upload Forms – manual submissions; Email – attachment intake; WhatsApp – document ingestion; Google Gemini – AI extraction & understanding; MongoDB – structured storage; n8n – orchestration, validation, routing.

    Smart Logic & Reliability

    • Works with PDFs, images, and scans
    • Handles multi-page documents
    • Detects structured vs unstructured content
    • Produces consistent schemas for every document
    • Designed for high-volume ingestion
    • Ready for RAG or search-layer integrations

    Before

    Manual reading, copying, tagging, and filing of documents.

    After

    Upload a document → get structured, searchable data automatically.

    Tools Used

    n8n
    Google Gemini
    MongoDB
    Email & WhatsApp ingestion
    OCR & document parsing logic

    Our Process

    1

    Discover

    Mapped document handling bottlenecks across teams.

    2

    Design

    Created a multi-channel intake and AI extraction pipeline.

    3

    Build

    Integrated AI agents with structured data output.

    4

    Integrate

    Connected MongoDB for long-term storage.

    5

    Deploy

    Tuned extraction accuracy and schema consistency.

    Business Impact

    Eliminates manual document processing

    Centralizes document intelligence

    Enables fast search and retrieval

    Improves data accuracy and consistency

    Scales to thousands of documents

    Foundation-ready for AI search and RAG systems

    "This Document Automation System transforms raw files into structured intelligence automatically. By combining multi-channel intake, AI-powered understanding, and clean MongoDB storage, it gives businesses a scalable, reliable way to process documents without human effort."

    Want a system like this for your business?

    Let’s build it.