PaperTrail AI is a full-stack AI-powered document intelligence platform designed to transform unstructured business documents into structured, actionable intelligence.
Users can upload documents such as PDFs and images, which are processed in memory using Google Gemini Vision AI. The system analyzes the document and extracts important business information including obligations, deadlines, payment amounts, contacts, organizations, risks, recommendations, and actionable tasks.
Instead of simply storing uploaded files, PaperTrail AI converts documents into structured intelligence that can be searched, tracked, scheduled, and acted upon through a centralized operations dashboard.
The platform is designed around a privacy-focused zero-storage workflow where original uploaded documents are processed temporarily in memory and discarded after analysis. Only the structured metadata and extracted intelligence required by the application are persisted in MongoDB Atlas.
PaperTrail AI follows a zero-storage approach for original uploaded documents. The uploaded PDF/image exists temporarily in memory during the processing pipeline while Gemini Vision AI analyzes the document and extracts structured intelligence.
After processing, the original file is discarded. Only useful structured information is persisted in MongoDB Atlas.
Privacy-Focused Data Flow
1Uploaded File
2Temporary in-memory processing
3Gemini AI analysis
4Structured JSON intelligence
5MongoDB Atlas
6Original document discarded
Persisted Intelligence (MongoDB Atlas)
Document metadata
Summary
Extracted obligations
Deadlines
Payment information
Contacts & Organizations
Tasks
Risks & Recommendations
Classification & Confidence scores
Key Features
AI Document Intelligence — Upload business documents and automatically extract meaningful structured information using Gemini Vision AI.
Multimodal Document Processing — Support PDF and image-based documents such as PNG, JPG and JPEG.
Obligation Extraction — Identify important contractual or business obligations automatically.
Deadline Detection — Extract dates, renewal dates, due dates, and other time-sensitive obligations.
Payment Intelligence — Detect payment amounts and payment-related obligations from uploaded documents.
Risk Detection — Surface important risks, clauses, and potentially critical document conditions.
Automatic Task Generation — Convert extracted obligations into actionable tasks with descriptions, priority, categories, and due dates.
Action Lifecycle — Allow tasks to move through pending, completed, and archived states.
Notifications — Generate notifications for important payment obligations, deadlines, and extracted events.
Action Calendar — Present extracted deadlines and actions in a calendar-oriented workflow.
Zero-Storage Processing — Original documents are processed in memory and discarded after analysis. Only structured intelligence and metadata are persisted.
MongoDB Intelligence Storage — Persist structured document metadata, extracted intelligence, tasks, deadlines, contacts, risks, recommendations, and related information in MongoDB Atlas.
Document Deletion — Allow users to permanently delete documents and automatically remove their linked tasks and notifications.
Manual Force Deletion — Provide a clear user-controlled deletion mechanism for permanently removing stored structured records when required.
AI Chat — Allow users to interact with extracted document intelligence through an AI-assisted interface.
Audit Trail — Track important application and document-related actions for transparency and accountability.
Secure Authentication — Use Firebase Authentication and protected application routes to isolate user data.
Responsive Dashboard — Provide a responsive executive operations dashboard that works across desktop, tablet, and mobile devices.
My Role & Engineering Contributions
•Designed and developed the complete PaperTrail AI full-stack application from architecture and UI design through deployment.
•Built the document upload and in-memory processing workflow for PDF and image-based documents.
•Integrated Google Gemini Vision AI for multimodal document analysis and structured information extraction.
•Designed MongoDB Atlas schemas for documents, tasks, notifications, users, and audit records.
•Implemented Firebase Authentication and protected application routes.
•Built automated extraction of obligations, deadlines, payment information, contacts, organizations, risks, recommendations, and action items.
•Implemented an action-item lifecycle with pending, completed, and archived states.
•Built deadline-aware notifications and an action calendar for extracted obligations.
•Implemented document deletion with cascading removal of linked tasks and notifications.
•Implemented zero-storage document processing so original uploaded files are not permanently stored.
•Built dashboard telemetry showing scanned documents, pending actions, completed actions, and active notifications.