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Sample SRD - Loanmind AI

📌 This is the full, unabbreviated Software Requirement Document DeX generated for Loanmind AI, continuing directly from the Sample BRD - Loanmind AI. It translates the business requirements into system architecture, infrastructure configuration, data models, API specifications, and non-functional requirements.

For the trimmed, Contractor Sync version of this document, see Specification - FSD.

1. Document Information​

Field Details
Project Name LoanMind AI
Version 1.0
Created Date June 09, 2026
Last Updated June 09, 2026
Author Senior Software Architect
Status Draft

2. Introduction​

2.1 Purpose​

The purpose of this document is to provide a detailed description of the requirements for the LoanMind AI platform. LoanMind AI is an intelligent, AI-driven lending assistant designed to automate and streamline the Micro, Small & Medium Enterprises (MSME) and Credit Monitoring Arrangement (CMA) business loan processing lifecycle. It aims to eliminate administrative bottlenecks, accelerate credit assessment, and reduce the high friction costs associated with traditional underwriting, thereby making formal credit more accessible to MSMEs.

2.2 Scope​

The scope of the LoanMind AI system encompasses the entire pre-disbursement journey of a business loan application, from initial document submission to the generation of a final, bank-ready credit assessment report.

In Scope:

  • Secure user (applicant) registration and profile management.
  • Automated ingestion, OCR, classification, and data extraction from applicant-submitted documents.
  • A dynamic, context-aware question-and-answer system to fill information gaps.
  • An interactive voice-based interview module for conversational data collection.
  • Two distinct processing workflows: a free "MSME Analyzer" for smaller loans and a premium "CMA Project" workflow for complex applications.
  • Automated generation of a suite of business health and credit analysis reports in Markdown format.
  • A structured Document Management System (DMS) using Alfresco for auditable storage of all application-related artifacts.
  • A Flutter-based mobile application for the applicant's journey and a web-based portal for administrative/bank officer access.

Out of Scope:

  • Direct integration with bank Core Banking Systems (CBS) for loan disbursement.
  • Post-disbursement loan monitoring and management.
  • Real-time credit bureau score fetching (e.g., CIBIL). This can be a future integration.
  • Legal and compliance verification of property or asset documents.

2.3 Definitions & Acronyms​

Term Definition
AI Artificial Intelligence, referring specifically to the use of Large Language Models (LLMs) like Google Gemini.
API Application Programming Interface.
CMA Credit Monitoring Arrangement. A detailed financial report required by banks for large business loans.
DMS Document Management System. In this project, refers to Alfresco Content Services.
DSCR Debt Service Coverage Ratio. A measure of a company's available cash flow to pay current debt obligations.
FastAPI A modern, fast (high-performance) web framework for building APIs with Python.
Flutter Google's UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase.
JSON JavaScript Object Notation. A lightweight data-interchange format.
JWT JSON Web Token. A compact, URL-safe means of representing claims to be transferred between two parties.
MSME Micro, Small, and Medium Enterprises.
OCR Optical Character Recognition. The process of converting images of typed, handwritten, or printed text into machine-encoded text.
PostgreSQL An open-source object-relational database system.
RBAC Role-Based Access Control.
SRD Software Requirement Document.

3. System Overview​

3.1 System Architecture​

LoanMind AI is designed with a modern, scalable, and service-oriented architecture. The system is decoupled into a frontend client and a backend API, which orchestrates various services.

  • Frontend (Flutter): A cross-platform mobile application serves as the primary interface for MSME applicants. It manages the user journey, document uploads, and the interactive voice interview.
  • Backend (FastAPI): A Python-based API server that acts as the system's core. It handles business logic, user authentication, data processing, and coordinates with all other services.
  • Database (PostgreSQL): The primary relational database for storing structured application data, user information, and system metadata.
  • Document Management (Alfresco): A dedicated DMS for storing, versioning, and managing all uploaded documents and generated reports, ensuring a clear audit trail.
  • AI Service (Google Gemini): An external LLM API service used for OCR data extraction validation, dynamic question generation, and the core report generation pipeline.
  • Asynchronous Task Queue (Celery with RabbitMQ/Redis): Handles long-running, resource-intensive tasks like document OCR, AI-powered analysis, and report generation without blocking the main API, ensuring a responsive user experience.
  • Object Storage (AWS S3): Used as a temporary staging area for file uploads before they are processed and ingested into Alfresco.

3.2 System Context Diagram​

[MSME Applicant] -- (HTTPS) --> [Flutter Mobile App]
                                      |
                                      v (REST API over HTTPS/JWT)
+-------------------------------------------------------------------------+
|                                  LoanMind AI Backend                    |
|  +---------------------+        +-----------------------------------+   |
|  |     API Gateway     |        |       FastAPI Core Services       |   |
|  |      (Nginx)      | <------> | (Application, User, Report Logic) |   |
|  +---------------------+        +-----------------------------------+   |
|                                      |         ^         |              |
|          +---------------------------+---------+---------+-----------+  |
|          |                           |                   |           |  |
|          v                           v                   v           v  |
| +-----------------+         +-----------------+   +-----------------+ +--------+
| |  Task Queue     |         |    Database     |   | Document Store  | | AI     |
| |(Celery/RabbitMQ)|         |  (PostgreSQL)   |   |   (Alfresco)    | | Engine |
| +-----------------+         +-----------------+   +-----------------+ +--------+
|                                                                       | (Gemini)
+-------------------------------------------------------------------------+

4. System Configurations​

4.1 Development Environment​

Component Technology Version Purpose
OS Linux (Ubuntu) / macOS 22.04+ / Sonoma+ Consistent developer operating system.
Containerization Docker Engine 25.0+ To containerize services for consistency across environments.
Docker Compose 2.24+ To orchestrate multi-container local development.
Language (BE) Python 3.11.x Backend application language.
Framework (BE) FastAPI 0.111.0 High-performance API server.
Uvicorn 0.29.0 ASGI server for running FastAPI.
Language (FE) Dart 3.4.x Frontend application language.
Framework (FE) Flutter 3.22.x UI toolkit for the client application.
Database PostgreSQL 16.x Primary relational data storage.
Task Queue RabbitMQ 3.13.x Message broker for asynchronous tasks.
Celery 5.4.x Python distributed task queue.
DMS Alfresco Community 7.4.x Document and content management.
Code Editor VS Code Latest With extensions for Python, Dart, and Docker.
VCS Git 2.43+ Version control system.

4.2 Infrastructure Configuration​

Component Specification Notes
Cloud Provider Amazon Web Services (AWS) Chosen for its mature ecosystem of managed services.
VPC Custom VPC Isolated network with public and private subnets across multiple AZs for high availability.
Web/App Server AWS Fargate on ECS Serverless container orchestration for FastAPI services. Auto-scales based on CPU/Memory usage.
Reverse Proxy AWS Application Load Balancer (ALB) Manages ingress traffic, SSL termination, and distributes load to Fargate services.
Database Server AWS RDS for PostgreSQL Multi-AZ deployment for high availability and automated backups.
DMS Server Alfresco on EC2/EKS Alfresco deployed on EC2 instances or within an EKS cluster for more control over configuration.
Task Queue Broker Amazon MQ for RabbitMQ Managed message broker service.
Object Storage AWS S3 For temporary file uploads, static frontend assets, and backups.
CDN AWS CloudFront Caches and serves the Flutter web app (if built) and static assets globally for low latency.

4.3 Third-Party Integrations​

Service Purpose API Version Auth Method
Google AI Platform AI-powered analysis, Q&A generation, and report writing. Gemini API v1beta API Key (sent in x-goog-api-key header).
Alfresco Content Services Structured document storage, versioning, and retrieval. CMIS 1.1 / REST API v1 Basic Authentication or OAuth 2.0.
Twilio Media Streams Real-time speech-to-text transcription for voice interviews. v1 API Key & Secret.
SendGrid / AWS SES Transactional email services for user notifications. v3 (SendGrid) API Key.

4.4 Environment Variables​

Variable Description Example Value Required
ENVIRONMENT Deployment environment (e.g., dev, staging, prod) staging Yes
DATABASE_URL Connection string for the PostgreSQL database. postgresql://user:pass@host:port/dbname Yes
ALFRESCO_URL Base URL for the Alfresco repository API. https://alfresco.loanmind.ai/alfresco/api/-default-/public/cmis/versions/1.1/browser Yes
ALFRESCO_USER Username for Alfresco API authentication. admin Yes
ALFRESCO_PASSWORD Password for Alfresco API authentication. [SECRET] Yes
JWT_SECRET_KEY Secret key for signing and verifying JWTs. [RANDOM_HEX_STRING] Yes
JWT_ALGORITHM Algorithm used for JWT signing. HS256 Yes
GEMINI_API_KEY API Key for Google Gemini services. [SECRET] Yes
TWILIO_ACCOUNT_SID Account SID for Twilio services. ACxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Yes
TWILIO_AUTH_TOKEN Auth token for Twilio services. [SECRET] Yes
AWS_ACCESS_KEY_ID AWS access key for S3 and other services. [SECRET] Yes
AWS_SECRET_ACCESS_KEY AWS secret key. [SECRET] Yes
AWS_S3_UPLOAD_BUCKET Name of the S3 bucket for temporary uploads. loanmind-uploads-staging Yes
CELERY_BROKER_URL Connection URL for the RabbitMQ message broker. amqp://user:pass@host:5672// Yes
CELERY_RESULT_BACKEND URL for storing Celery task results (can use DB or Redis). db+postgresql://... Yes

4.5 Deployment Configuration​

Environment URL (API / App) Server Specs Notes
Development localhost:8000 / localhost Local Machine via Docker Compose For local development and testing by engineers.
Staging api.staging.loanmind.ai / app.staging.loanmind.ai AWS Fargate: 2 Tasks, 1 vCPU, 2GB RAM each. RDS: db.t3.medium. Pre-production environment for QA testing and UAT. Mirrors production setup.
Production api.loanmind.ai / app.loanmind.ai AWS Fargate: Auto-scaling from 4 to 20 Tasks, 2 vCPU, 4GB RAM each. RDS: db.m5.large (Multi-AZ). Live environment for end-users. Monitored 24/7 with automated alerting.

5. Functional Requirements​

5.1 User & Application Management​

Req ID Requirement Priority Acceptance Criteria
SRD-UAM-001 User Registration Must Have Users can register using a mobile number and OTP verification. A user record is created in the database.
SRD-UAM-002 User Authentication Must Have Registered users can log in using their mobile number and OTP. Upon success, a JWT is issued.
SRD-UAM-003 Create New Application Must Have Authenticated users can start a new loan application, choosing between "MSME" or "CMA" workflow. An application record is created with a unique application_number and INITIATED status.
SRD-UAM-004 View Application Status Must Have Users can view the status of their ongoing and past applications on a dashboard. The status reflects the Flutter State Machine.

5.2 Document Processing Module​

Req ID Requirement Priority Acceptance Criteria
SRD-DPM-001 Document Upload Must Have Users can upload documents (PDF, JPG, PNG) for a specific application. Files are first uploaded to a temporary S3 bucket.
SRD-DPM-002 Asynchronous Document Processing Must Have Upon upload, a background task is triggered to process the document. The API should return an immediate "processing" status to the user.
SRD-DPM-003 AI-Powered Document Classification Must Have The system must automatically identify the type of document (Aadhaar, PAN, Bank Statement, etc.) with >95% accuracy.
SRD-DPM-004 AI-Powered Data Extraction (OCR) Must Have The system must extract key fields from each document type into a structured JSON object. For a PAN card, this includes PAN, Name, DOB.
SRD-DPM-005 Store in DMS (Alfresco) Must Have After successful processing, the original file and its extracted JSON data are moved from S3 to the correct folder in the Alfresco repository, following the defined schema.
SRD-DPM-006 Update Application Status Must Have The frontend polls a status endpoint. As documents are successfully processed and verified, the UI updates a checklist to show completion.

5.3 Interactive Interview & Analysis Module​

Req ID Requirement Priority Acceptance Criteria
SRD-AIM-001 Dynamic Question Generation Must Have After initial documents are processed, the system uses Gemini to analyze the extracted data and business context to generate a set of targeted questions to fill information gaps.
SRD-AIM-002 Voice-based Interview Should Have The Flutter app presents the questions and allows the user to respond via voice. The system transcribes the audio to text in real-time.
SRD-AIM-003 Conversation Log Upload Must Have The final, transcribed Q&A is compiled into a master-conversation.md file and uploaded to the application's Alfresco folder.
SRD-AIM-004 Trigger Report Generation Pipeline Must Have The upload of master-conversation.md automatically triggers the background report generation pipeline.
SRD-AIM-005 Sequential Report Generation Must Have The 6 MSME analysis reports are generated sequentially, with the context of each previous report being used in the prompt for the next one.
SRD-AIM-006 Final Report Storage Must Have All generated .md reports are stored in the msme_reports subfolder within the application's Alfresco directory.
SRD-AIM-007 Update Status to COMPLETED Must Have Once the final report (msme_loan_summary.md) is generated, the application status is updated to COMPLETED.

5.4 Report Dashboard & Export​

Req ID Requirement Priority Acceptance Criteria
SRD-RDE-001 View Generated Reports Must Have When an application is COMPLETED, the user can view the list of generated reports in the Flutter app.
SRD-RDE-002 Render Markdown Reports Must Have The app must correctly render the Markdown content of the reports, including headers, lists, and tables.
SRD-RDE-003 Export to PDF Should Have Users should have an option to export all generated reports combined into a single, well-formatted PDF document.

6. Non-Functional Requirements​

6.1 Performance Requirements​

Req ID Requirement Metric Target
NFR-P-001 API Response Time (p95) Milliseconds < 400ms for synchronous endpoints.
NFR-P-002 Document Processing Time Seconds < 90 seconds from upload to Alfresco ingestion for a typical 10-page bank statement.
NFR-P-003 Full Report Suite Generation Minutes < 5 minutes from master-conversation.md upload to final report generation.
NFR-P-004 Concurrent Users Number The system must support 1,000 concurrent active users during peak hours without performance degradation.

6.2 Security Requirements​

Req ID Requirement Standard Priority
NFR-S-001 Data Encryption at Rest AES-256 Must Have
NFR-S-002 Data Encryption in Transit TLS 1.3 Must Have
NFR-S-003 Authentication & Authorization JWT with short-lived access tokens and long-lived refresh tokens. RBAC for different user types (Applicant, Admin). Must Have
NFR-S-004 Input Validation OWASP Standards Must Have
NFR-S-005 Secret Management AWS Secrets Manager / HashiCorp Vault Must Have
NFR-S-006 PII Data Protection Compliance with India's DPDP Act Must Have

6.3 Scalability Requirements​

Req ID Requirement Notes
NFR-SC-001 Horizontal Scaling The backend application services must be stateless and horizontally scalable. Using AWS Fargate enables this automatically.
NFR-SC-002 Database Scalability The database will be configured with read replicas to offload read-heavy queries from the primary instance.
NFR-SC-003 Asynchronous Processing The use of a message queue (RabbitMQ) allows for the scaling of worker nodes independently of the web-facing API servers to handle variable loads of document processing tasks.

6.4 Reliability & Availability​

Req ID Requirement Target
NFR-R-001 System Uptime SLA 99.9%
NFR-R-002 Recovery Time Objective (RTO) < 1 hour
NFR-R-003 Recovery Point Objective (RPO) < 15 minutes
NFR-R-004 Data Backups Automated daily backups of the PostgreSQL database and Alfresco content store, with point-in-time recovery enabled.

7. Data Requirements​

7.1 Data Model Overview​

The data model is centered around the Application, which links a User to a collection of Documents and Reports.

  • User: Represents an applicant or an administrator. Identified by a mobile number.
  • Application: The core entity representing a single loan application. It has a status that follows the defined state machine.
  • Document: Represents a file uploaded by the user. It stores metadata, its type, its location in Alfresco, and the extracted JSON data.
  • Report: Represents a markdown file generated by the AI pipeline. It is linked to an application.

7.2 Database Schema (Key Tables)​

Table Description Key Fields
users Stores user account information. id (PK), mobile_number (UNIQUE), full_name, password_hash, role, created_at
applications Tracks each loan application. id (PK), application_number (UNIQUE), user_id (FK), workflow_type (MSME/CMA), status, created_at, updated_at
documents Metadata for each uploaded document. id (PK), application_id (FK), document_type, original_filename, alfresco_node_ref, status, extracted_data (JSONB), uploaded_at
conversation_logs Stores the master Q&A transcript. id (PK), application_id (FK), alfresco_node_ref, content (TEXT), created_at

8. API Specifications​

8.1 API Overview​

All endpoints will be versioned under /api/v1/.

Endpoint Method Description Auth Required
/api/v1/auth/register POST Registers a new user via mobile and OTP. No
/api/v1/auth/login POST Authenticates a user and returns a JWT. No
/api/v1/applications POST Creates a new loan application. Yes
/api/v1/applications/{app_num} GET Retrieves the details and status of an application. Yes
/api/v1/documents/upload/{app_num} POST Uploads a document for an application. Yes
/api/v1/documents/status/{app_num} GET Gets the processing status of all documents for an application. Yes
/api/v1/conversation/upload/{app_num} POST Uploads the master-conversation.md file. Yes
/api/v1/reports/{app_num} GET Lists all generated reports for a completed application. Yes
/api/v1/reports/{app_num}/{report_name} GET Downloads a specific report file. Yes

8.2 API Authentication​

The API will be secured using JSON Web Tokens (JWT).

  1. A user authenticates via the /login endpoint.
  2. The server returns a short-lived access token (~15 minutes) and a long-lived refresh token (~30 days).
  3. The client sends the access token in the Authorization: Bearer <token> header for all protected requests.
  4. If the access token is expired, the client uses the refresh token to obtain a new access token without requiring the user to log in again.

9. User Interface Requirements​

9.1 UI Design Principles​

  • Simplicity & Clarity: The interface should be intuitive, with clear instructions and minimal clutter, guiding the user through a complex process.
  • Mobile-First: The primary interface is a mobile app. All interactions must be optimized for smaller touchscreens.
  • Progressive Disclosure: Show only the information and actions relevant to the user's current step to avoid overwhelming them.
  • Accessibility: Adhere to WCAG 2.1 AA standards for colors, contrast, and screen reader compatibility.

9.2 Screen Inventory (Flutter App)​

Screen Description Key Components
Splash/Login Initial screen for authentication. Mobile number input, OTP field, Login button.
Dashboard Main screen after login, shows a list of applications. List of applications with their status, "Start New Application" button.
Document Upload Interface for uploading required documents. Dynamic checklist of required documents, upload buttons for each, progress indicators, status icons (pending, verified, error).
Voice Interview Screen for the interactive Q&A session. Display of the current question, "Record Answer" button, audio waveform visualizer, transcript preview.
Report Viewer Displays the final generated reports. List of available reports, Markdown renderer view, "Export to PDF" button.

9.3 Responsive Design Requirements​

The primary client is a Flutter mobile application designed for standard iOS and Android screen sizes. A future web portal for bank officers must be responsive and functional across major desktop browser resolutions (1280px, 1440px, 1920px).

10. Testing Requirements​

Test Type Scope Tools Coverage Target
Unit Testing Backend: Individual functions, classes. Frontend: Widgets, business logic. Pytest (BE), Flutter test package (FE) 85% line coverage
Integration Testing Interaction between API services, database, and Alfresco. Pytest with test containers, Flutter integration_test 90% of API endpoints
End-to-End (E2E) Testing Critical user flows from registration to report viewing. Flutter integration_test, potentially Playwright for a web admin portal. 100% of critical paths
Performance Testing API load testing, document processing throughput. Locust, k6 Meet NFR-P targets
Security Testing Vulnerability scanning, penetration testing. OWASP ZAP, Snyk, manual penetration tests before major releases. No critical or high-severity vulnerabilities

11. Deployment & DevOps​

11.1 CI/CD Pipeline​

A CI/CD pipeline will be implemented using GitHub Actions.

  1. On Pull Request to main:
    • Lint code (Flake8 for Python, Dart Analyzer for Flutter).
    • Run all unit and integration tests.
    • Build Docker image for backend.
    • Build Flutter app.
    • Report status back to the PR. Merging is blocked on failure.
  2. On Merge to main:
    • All previous steps are re-run.
    • Push Docker image to AWS ECR.
    • Deploy the new image to the Staging environment on AWS Fargate.
    • Run E2E tests against the Staging environment.
  3. On Git Tag (e.g., v1.1.0):
    • A manual approval step is required.
    • Upon approval, the validated image from Staging is promoted and deployed to the Production environment.

11.2 Monitoring & Logging​

Tool Purpose Configuration
AWS CloudWatch Logs Centralized logging for all services (FastAPI, Celery workers, Fargate). All application logs (stdout/stderr) will be streamed directly to CloudWatch.
AWS CloudWatch Metrics Infrastructure and application performance monitoring. Monitor CPU/Memory usage of Fargate tasks, RDS connections, ALB request counts, and 5XX error rates.
AWS CloudWatch Alarms Automated alerting. Alarms configured for high error rates (>1%), high CPU utilization (>80%), and application health check failures. Notifications sent to Slack and PagerDuty.
Sentry / Datadog APM Application Performance Monitoring (APM) and error tracking. Integrated into the FastAPI application to trace requests, identify performance bottlenecks, and capture detailed error reports with stack traces.

12. Appendix​

A. Glossary​

See Section 2.3 Definitions & Acronyms.

B. Reference Documents​

  • LoanMind AI Project Proposal & Vision Document
  • UI/UX Design Mockups (Link to Figma)
  • Alfresco Content Model Definition

C. Revision History​

Version Date Author Changes
1.0 June 09, 2026 Senior Software Architect Initial creation of the Software Requirement Document.

What's Next​

➡️ Sample FSD - Loanmind AI - See the screen-by-screen functional specifications built on top of this architecture.