Skip to content

BUSINESS_REQUIREMENTS.md

Latest commit

 

History

History
139 lines (99 loc) · 9.81 KB

BUSINESS_REQUIREMENTS.md

File metadata and controls

139 lines (99 loc) · 9.81 KB
Raw

Business Requirement Document: IntelliDocs

Version: 1.0
Date: 2023-10-27

1. Introduction

1.1 Purpose

This document outlines the business requirements for IntelliDocs, an intelligent document processing and management system. Its purpose is to provide a clear and comprehensive foundation for all project stakeholders, including developers, project managers, and business analysts, ensuring that the final product aligns with the strategic goals of the organization.

1.2 Project Overview & Goals

In many organizations, a significant amount of time and resources are spent on manually processing, categorizing, and extracting information from a high volume of documents like invoices, receipts, and reports. This manual process is often slow, error-prone, and difficult to scale.

IntelliDocs is a back-end service designed to automate this entire workflow. By leveraging Artificial Intelligence (AI) and Machine Learning (ML), the system will automatically read uploaded documents, extract key-value data, categorize them into predefined pools, and provide a robust API for management and analysis. The primary goal is to increase efficiency, reduce manual errors, and provide valuable, real-time insights into the document ecosystem.

1.3 Intended Audience

This document is intended for:

  • Project Sponsors & Stakeholders: To understand the project's scope and business value.
  • Development Team: To guide the design, development, and testing of the application.
  • Quality Assurance Team: To create test plans and validate that the system meets all specified requirements.
  • Project Managers: To manage the project's scope, schedule, and resources.

2. Business Problem & Opportunity

2.1 Business Problem

  • High Operational Cost: Manual data entry and document sorting require significant human labor, leading to high operational costs.
  • Prone to Human Error: Manual processing is susceptible to errors in data extraction and categorization, which can lead to incorrect data and poor business decisions.
  • Lack of Scalability: The manual process cannot easily scale to handle sudden increases in document volume.
  • Poor Data Accessibility: Information trapped in unstructured documents is not easily searchable or analyzable, leading to missed insights.

2.2 Opportunity

By developing IntelliDocs, we have the opportunity to:

  • Drastically Reduce Processing Time: Automate data extraction and categorization to process documents in seconds instead of minutes.
  • Improve Data Accuracy: Minimize human error by using AI models for consistent and accurate data handling.
  • Enable Real-Time Insights: Transform unstructured data into a structured, queryable format, accessible via a dashboard for real-time analytics.
  • Create a Scalable Solution: Build a system that can grow with the organization's needs.

3. Project Goals & Objectives

  • Goal 1: Automate the document lifecycle from upload to archival.
    • Objective: Reduce the average document processing time by at least 95% compared to the current manual baseline.
  • Goal 2: Ensure high accuracy in data extraction and categorization.
    • Objective: Achieve an auto-categorization accuracy of 90% or higher after the initial model training period.
  • Goal 3: Provide a centralized and user-friendly platform for document management.
    • Objective: Deliver a comprehensive API that allows for document searching, filtering, validation, and retrieval.
  • Goal 4: Offer actionable insights into document processing metrics.
    • Objective: Develop a real-time analytics dashboard that tracks key performance indicators (KPIs) like total documents processed, accuracy rates, and average processing times.

4. Scope

4.1 In-Scope Features

  1. Document Upload: Users can upload documents in various formats (PDF, DOCX, TXT, XLSX, MD).
  2. Asynchronous AI Processing: Uploaded documents are placed in a queue for background processing to avoid blocking user requests.
  3. Automated Data Extraction: The system automatically extracts key-value pairs (KVPs) from documents.
  4. Automated Document Categorization: The system automatically assigns a category (e.g., "Invoice", "Receipt") to each document.
  5. Manual Validation & Correction:
    • Users can manually correct or update the extracted KVPs.
    • Users can re-categorize a document if the AI classifies it incorrectly.
  6. Document Search & Retrieval:
    • Users can search for documents by filename.
    • Users can retrieve a list of all documents or filter them by category.
    • Users can fetch the detailed information of a single document by its ID.
  7. Original File Download: Users can download the original file that was uploaded.
  8. Analytics Dashboard: An API endpoint provides aggregated statistics for a real-time dashboard, including processing accuracy and document pool metrics.
  9. System Health Check: An endpoint is available to monitor the health and availability of the service.

4.2 Out-of-Scope Features (for this version)

  • User Authentication & Role-Based Access Control (RBAC): All API endpoints are currently public.
  • Multi-Tenancy: The system does not support separate data environments for different clients or departments.
  • Front-End User Interface: This project covers only the back-end API service.
  • Advanced AI Model Fine-Tuning Interface: While data for fine-tuning is collected, there is no user-facing interface to trigger or manage the tuning process.

5. Functional Requirements

Feature ID Feature User Story
FR-001 Health Check As a system administrator, I want a health check endpoint so that I can monitor the service's availability.
FR-002 Document Upload As a user, I want to upload a document via an API so that it can be processed by the system.
FR-003 Asynchronous Processing As a user, I want document processing to happen in the background so that the API responds quickly without waiting for the AI to finish.
FR-004 Get Document List As a user, I want to retrieve a list of all documents, with the option to filter by category, so that I can see the documents in the system.
FR-005 Get Document Details As a user, I want to retrieve all the data for a single document by its ID so that I can inspect its contents and status.
FR-006 Download Original File As a user, I want to download the original file for a document so that I can view its source content.
FR-007 Update KVPs As a user, I want to manually correct the extracted key-value pairs for a document so that the data is accurate.
FR-008 Re-categorize Document As a user, I want to change a document's category and provide a reason so that I can correct classification errors for model fine-tuning.
FR-009 Re-process Document As a user, I want to re-trigger the AI processing for a document so that I can handle initial processing failures or apply updated models.
FR-010 Search Documents As a user, I want to search for documents by filename so that I can quickly find a specific file.
FR-011 Dashboard Statistics As a user, I want to get aggregated statistics about the document library so that I can monitor the system's overall performance.

6. Non-Functional Requirements

  • Performance: The API should have an average response time of less than 200ms for non-processing requests. Asynchronous document processing should ideally be completed within 30 seconds on average.
  • Scalability: The system architecture must support horizontal scaling to handle an increasing volume of documents.
  • Security: All sensitive information, such as API keys and database credentials, must not be hard-coded and should be managed via environment variables.
  • Maintainability: The codebase must be modular (e.g., using Flask Blueprints) and follow standard Python coding practices (PEP 8) to ensure ease of maintenance.
  • Usability: The API must be clearly documented, with an API_REFERENCE.md file that details all endpoints, parameters, and example responses.

7. Stakeholders

  • Project Sponsor: Executive management overseeing the project.
  • Business Owner: The primary business-side leader responsible for the project's success.
  • Development Team: The engineers responsible for building and maintaining the application.
  • End Users: The individuals or systems that will interact with the IntelliDocs API.

8. Assumptions & Constraints

8.1 Assumptions

  • The application will be deployed in a Nix-based environment as configured in dev.nix.
  • The required AI/ML models for text extraction and categorization are available and accessible to the application.
  • A MongoDB instance is available and the connection URI is provided via environment variables.

8.2 Constraints

  • The project is dependent on the performance and accuracy of the external AI models it utilizes.
  • The system is designed to support a specific set of file formats as detailed in the requirements.
  • The initial version does not include user authentication, meaning all data is accessible to any user with API access.