Aicurate - AI Investment Platform

A revolutionary AI-powered platform that transforms how startups connect with investors. Leveraging Google's Gemini AI, Aicurate automates startup evaluation, generates comprehensive investment reports, and provides data-driven insights that accelerate funding decisions while reducing due diligence time from weeks to hours.

Introduction

Aicurate is a cutting-edge AI-powered investment platform that revolutionizes startup-investor connections through intelligent automation. Built on modern web technologies and powered by Google's Gemini AI, it transforms the traditional due diligence process by providing instant, comprehensive analysis of startup submissions.

The platform processes multimodal data (documents, videos, financial models) to generate detailed investment reports with 100+ metrics, enabling investors to make faster, more informed decisions while helping founders present their ventures more effectively. By automating the analysis pipeline, Aicurate reduces evaluation time from weeks to hours while maintaining institutional-grade accuracy.

Demo

Live Demo: Link to Demo
Video Walkthrough: Link to Video
Pitch Deck: Download PDF

Screenshots Gallery

Homepage & Navigation

Clean, modern homepage with role selection and navigation

Investor Experience

Comprehensive investor dashboard with AI-powered insights and analytics

Curated startup listings with filtering and search capabilities

Investor preference settings for personalized recommendations

AI Analysis & Reports

AI-generated deal insights and investment analysis

Interactive charts and data visualizations

Investment Decision Making

Investment decision interface with AI recommendations

Inspiration

The traditional startup-investor matching process is often inefficient, time-consuming, and lacks data-driven insights. Founders struggle to present their ventures comprehensively, while investors face information overload when evaluating opportunities. Aicurate was built to address these challenges by:

Automating Analysis: Using AI to process and analyze startup submissions comprehensively
Standardizing Information: Creating structured, comparable data for investment decisions
Enhancing User Experience: Providing intuitive interfaces for all user types
Enabling Data-Driven Decisions: Offering detailed metrics and scoring systems

What It Does

For Founders

Startup Submission Management: Create and manage multiple startup submissions
File Upload System: Support for pitch decks, videos, financial models, and documents
AI-Powered Analysis: Automated processing and report generation
Real-time Status Tracking: Live updates on submission processing
Comprehensive Dashboards: Track submissions, funding goals, and progress

For Investors

Intelligent Startup Discovery: AI-curated startup recommendations
Detailed Analysis Reports: Comprehensive 100+ metric evaluations
Investment Management: Track and manage investment portfolios
Advanced Filtering: Find startups based on sector, score, and preferences
Deal Insights: AI-generated insights and risk assessments

For Administrators

Platform Management: Oversee users, startups, and investments
Analytics Dashboard: Platform-wide statistics and insights
User Management: Manage user roles and permissions
Content Moderation: Review and approve submissions

AI-Powered Features

Multimodal Analysis: Process text, images, videos, and documents
Intelligent Scoring: 10+ scoring dimensions for comprehensive evaluation
Risk Assessment: Automated risk identification and flagging
Market Analysis: TAM/SAM/SOM calculations and competitor analysis
Deal Memo Generation: Automated investment memo creation

How We Built It

Architecture Overview

graph TD
    %% ================== GROUPS ==================
    subgraph client["Client Layer"]
        U["User Browser (Founder / Investor / Admin)"]
    end

    subgraph edge["Edge Layer"]
        CDN["CDN / WAF"]
        LB["API Gateway / Load Balancer"]
    end

    subgraph app["Application Layer"]
        NGINX["Nginx Reverse Proxy"]
        subgraph flask["Flask App (Aicurate)"]
            direction TB
            WEB["Web Layer (Blueprints)"]
            subgraph svc["Service Layer"]
                direction TB
                AUTHZ["Auth / Session Manager"]
                SUBM["Submission Handler"]
                RECO["Recommendation Engine"]
            end
            Q["In-Memory Job Queue"]
            W["AI Worker (Async)"]
        end
    end

    subgraph ai["AI Layer"]
        GEM["Gemini / Vertex AI (Multimodal)"]
    end

    subgraph firebase["Firebase Layer"]
        FS["Firestore (Insights + Metadata)"]
        ST["Firebase Storage (Pitch Decks, XLSX, Video)"]
    end

    %% ================== FLOWS ==================
    U -->|"HTTPS Requests"| CDN --> LB --> NGINX --> WEB
    U -->|"Auth SDK"| AUTHZ

    WEB --> AUTHZ
    WEB --> SUBM
    SUBM -->|"enqueue"| Q
    Q --> W
    W -->|"AI Analysis"| GEM
    W -->|"Save Report / Metrics"| FS
    SUBM -->|"File Upload"| ST
    W -->|"Store Thumbnails / Derived"| ST
    FS --> RECO
    RECO --> FS

    %% ================== CLASSES (using old theme) ==================
    %% Blue
    classDef client   fill:#93C5FD,stroke:#1D4ED8,stroke-width:2.2,color:#0B1B32;
    %% Amber
    classDef edge     fill:#FCD34D,stroke:#B45309,stroke-width:2.2,color:#2B1D07;
    %% Teal
    classDef app      fill:#5EEAD4,stroke:#0F766E,stroke-width:2.2,color:#053A37;
    %% Blue (Flask app inner)
    classDef flask    fill:#93C5FD,stroke:#1E40AF,stroke-width:2.2,color:#0B1B32;
    %% Light Blue (service layer)
    classDef svc      fill:#DBEAFE,stroke:#1E40AF,stroke-width:2.2,color:#0B1B32;
    %% Green
    classDef ai       fill:#86EFAC,stroke:#15803D,stroke-width:2.2,color:#062812;
    %% Pink
    classDef firebase fill:#F9A8D4,stroke:#9D174D,stroke-width:2.2,color:#3B0A1E;

    class U client
    class CDN,LB edge
    class NGINX,WEB,Q,W app
    class flask flask
    class AUTHZ,SUBM,RECO svc
    class GEM ai
    class FS,ST firebase

    %% ================== GROUP STYLES (matching old look) ==================
    style client   fill:#E2E8F0,stroke:#334155,stroke-width:2
    style edge     fill:#E5E7EB,stroke:#374151,stroke-width:2
    style app      fill:#E5E7EB,stroke:#374151,stroke-width:2
    style flask    fill:#DBEAFE,stroke:#1E40AF,stroke-width:1.8,stroke-dasharray:4 3
    style svc      fill:#DBEAFE,stroke:#1E40AF,stroke-width:1.6,stroke-dasharray:4 3
    style ai       fill:#E5E7EB,stroke:#374151,stroke-width:2
    style firebase fill:#E5E7EB,stroke:#374151,stroke-width:2

    %% ================== LINKS ==================
    linkStyle default stroke:#334155,stroke-width:2,opacity:0.95

Core Components

1. AI Processing Pipeline

Multimodal file processing (PDF, video, audio, documents)
Gemini AI integration for content analysis
Structured report generation with 100+ metrics
Automated scoring and risk assessment

2. User Management System

Firebase Authentication for secure login
Role-based access control (Founder, Investor, Admin)
Session management and security

3. File Upload & Processing

Support for multiple file types and formats
Secure file storage and organization
Content extraction and analysis

4. Real-time Processing

Asynchronous AI processing
Status tracking and updates
Error handling and recovery

Data Flow

graph TD
    %% ================== GROUPS ==================
    subgraph browser["User's Browser"]
        A["User (Founder / Investor / Admin)"]
    end

    subgraph cloud["Cloud Services"]
        B["Firebase Auth"]
        C["Firestore Database"]
        D["Google AI Platform (Gemini API)"]
    end

    subgraph app["Application Server (Docker)"]
        E["Nginx Web Server"]
        subgraph flaskapp["Flask Application (Aicurate)"]
            direction LR
            F["Web Layer (Blueprints)"]
            subgraph svc["Service Layer"]
                direction TB
                G["Core Services (Firebase • Files)"]
                H["Processing Pipeline"]
            end
            I["Async AI Worker (Background Thread)"]
        end
    end

    %% ================== FLOWS ==================
    A -- "(1) HTTPS Requests" --> E
    E -- "Reverse Proxy →" --> F

    A -- "(2) Auth via SDK" --> B
    F -- "Verify token / manage session" --> B

    F -- "Use" --> G
    G -- "CRUD" --> C

    F -- "(3) Queue startup submission" --> H
    H -- "Add job to in-memory queue" --> I

    I -- "(4) Upload files + prompt" --> D
    D -- "(5) Structured JSON" --> I
    I -- "(6) Save report + status" --> C

    %% ================== NODE CLASSES (darker fills + strong borders) ==================
    classDef user     fill:#93C5FD,stroke:#1D4ED8,stroke-width:2.2,color:#0B1B32;     %% Blue-300 fill, Blue-700 border
    classDef firebase fill:#FCD34D,stroke:#B45309,stroke-width:2.2,color:#2B1D07;     %% Amber-300 fill, Amber-700 border
    classDef googleai fill:#86EFAC,stroke:#15803D,stroke-width:2.2,color:#062812;     %% Green-300 fill, Green-700 border
    classDef nginx    fill:#5EEAD4,stroke:#0F766E,stroke-width:2.2,color:#053A37;     %% Teal-300 fill, Teal-700 border
    classDef flask    fill:#93C5FD,stroke:#1E40AF,stroke-width:2.2,color:#0B1B32;     %% Blue-300 fill, Indigo-800 border
    classDef worker   fill:#F9A8D4,stroke:#9D174D,stroke-width:2.2,color:#3B0A1E;     %% Pink-300 fill, Rose-800 border

    class A user;
    class B,C firebase;
    class D googleai;
    class E nginx;
    class F,G,H flask;
    class I worker;

    %% ================== GROUP BOUNDARIES (darker + solid) ==================
    style browser fill:#E2E8F0,stroke:#334155,stroke-width:2,stroke-dasharray:0
    style cloud   fill:#E5E7EB,stroke:#374151,stroke-width:2,stroke-dasharray:0
    style app     fill:#E5E7EB,stroke:#374151,stroke-width:2,stroke-dasharray:0
    style flaskapp fill:#DBEAFE,stroke:#1E40AF,stroke-width:1.8,stroke-dasharray:4 3
    style svc      fill:#DBEAFE,stroke:#1E40AF,stroke-width:1.6,stroke-dasharray:4 3

    %% ================== LINKS (clearer + darker) ==================
    linkStyle default stroke:#334155,stroke-width:2,opacity:0.95

Sequence Diagram: Startup Submission & Analysis

This diagram shows the detailed, step-by-step sequence of events that occurs when a founder submits their startup for AI analysis.

sequenceDiagram
    participant Founder as Founder (Browser)
    participant Backend as Flask Backend
    participant Firestore as Firestore DB
    participant Queue as Processing Queue
    participant Worker as AI Worker (Thread)
    participant Gemini as Google AI (Gemini)
    participant Investor as Investor (Browser)

    Founder->>+Backend: 1. POST /founder/api/startup (with form data & files)
    Backend->>+Firestore: 2. Create submission doc (status: 'submitted')
    Firestore-->>-Backend: Returns submission_id
    Backend->>+Queue: 3. queue_submission(submission_id, data)
    Queue->>+Firestore: 4. Update submission status to 'queued'
    Firestore-->>-Queue: OK
    Queue-->>-Backend: Queued successfully
    Backend-->>-Founder: Returns { success: true }

    Note over Queue, Worker: Later, in a background thread...
    Queue->>Worker: 5. Dequeue job
    Worker->>+Firestore: 6. Update submission status to 'processing'
    Firestore-->>-Worker: OK

    Worker->>+Gemini: 7. Upload files & send prompt
    Gemini-->>-Worker: 8. Return structured JSON report

    Worker->>+Firestore: 9. Save report to 'startup_evaluation_reports'
    Firestore-->>-Worker: OK
    Worker->>+Firestore: 10. Update submission status to 'completed'
    Firestore-->>-Worker: OK

    Note over Investor, Backend: Much later...
    Investor->>+Backend: 11. GET /investor/startup/<id>
    Backend->>+Firestore: 12. Fetch submission & AI report
    Firestore-->>-Backend: Return documents
    Backend-->>-Investor: 13. Render page with report data

Challenges We Faced

Technical Challenges

Multimodal File Processing: Handling diverse file formats and extracting meaningful content
AI Integration: Managing API rate limits and ensuring reliable AI responses
Real-time Processing: Implementing asynchronous processing with status updates
Data Structure Design: Creating flexible schemas for varied startup data

User Experience Challenges

Complex Data Presentation: Making AI-generated insights accessible and actionable
File Upload Management: Handling large files and multiple uploads efficiently
Cross-Platform Compatibility: Ensuring consistent experience across devices

Scalability Challenges

Database Optimization: Efficiently querying and storing large amounts of data
AI Processing Queue: Managing concurrent AI processing requests
File Storage: Organizing and serving uploaded files efficiently

How to Run

Prerequisites

Python 3.9+
Docker & Docker Compose
Firebase project with Authentication and Firestore enabled
Google Gemini API key
Git

Quick Start with Docker (Recommended)

Clone the repository

git clone https://github.com/Ayush-projects/aicurate.git
cd aicurate

Configure environment variables

cp env.production.example .env
# Edit .env with your credentials

Run with Docker Compose

# Development mode
docker-compose -f docker-compose.dev.yml up --build

# Production mode
docker-compose up --build

Access the application
- Open http://localhost:5001 in your browser
- The application will be running with hot reload in development mode

Local Development Setup

Clone the repository

git clone https://github.com/Ayush-projects/aicurate.git
cd aicurate

Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```

Configure environment variables

cp env_example.txt .env
# Edit .env with your Firebase and Gemini credentials

Run the application
```
python run.py
```

Environment Configuration

Create a .env file with the following variables:

# Firebase Configuration
FIREBASE_API_KEY=your_api_key
FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
FIREBASE_PROJECT_ID=your_project_id
FIREBASE_STORAGE_BUCKET=your_project.appspot.com
FIREBASE_MESSAGING_SENDER_ID=your_sender_id
FIREBASE_APP_ID=your_app_id

# Firebase Admin SDK
FIREBASE_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----\nYOUR_PRIVATE_KEY_HERE\n-----END PRIVATE KEY-----\n"
FIREBASE_CLIENT_EMAIL=firebase-adminsdk-xxxxx@your-project.iam.gserviceaccount.com

# Gemini AI
GEMINI_API_KEY=your_gemini_api_key

# Flask Configuration
SECRET_KEY=your_secret_key_here
FLASK_ENV=development
HOST=0.0.0.0
PORT=5001

# Admin Configuration
ADMIN_EMAILS=admin@company.com,ceo@company.com

Docker Build & Make Commands

Quick Start with Make

The project includes a comprehensive Makefile for easy Docker management:

# Show all available commands
make help

# Start development environment
make dev

# Start production environment
make prod

# Build Docker images
make build

# Start services in background
make up

# Stop all services
make down

# View application logs
make logs

# Open shell in app container
make shell

# Run tests
make test

# Clean up containers and images
make clean

Docker Commands

Development Environment

# Start with hot reload
make dev
# OR
docker-compose -f docker-compose.dev.yml up --build

# Run in background
docker-compose -f docker-compose.dev.yml up -d --build

# View logs
docker-compose -f docker-compose.dev.yml logs -f

Production Environment

# Start production setup
make prod
# OR
docker-compose up --build

# Run with nginx reverse proxy
docker-compose up -d --build

# Scale the application
make scale
# OR
docker-compose up --scale app=3

Advanced Docker Commands

# Build only
make build
# OR
docker-compose build

# Restart services
make restart
# OR
docker-compose restart

# Show container status
make status
# OR
docker-compose ps

# Clean up everything
make clean
# OR
docker-compose down -v
docker system prune -f

Development & Testing

# Run tests in container
make test

# Open shell for debugging
make shell

# Update dependencies
make update-deps

# Backup logs
make backup-logs

# Security scan
make security-scan

# Performance testing
make perf-test

Docker Compose Files

docker-compose.yml - Production configuration with nginx
docker-compose.dev.yml - Development configuration with hot reload

Tech Stack

Backend

Python 3.9+: Core programming language
Flask 2.3.3: Web framework
Firebase Admin SDK: Backend authentication and database
Google Gemini AI: AI processing and analysis
Werkzeug: WSGI utilities

Frontend

HTML5/CSS3: Structure and styling
JavaScript: Interactive functionality
Tailwind CSS: Utility-first CSS framework
Jinja2: Template engine

AI & ML

Google Gemini API: Multimodal AI analysis
PyPDF2: PDF processing
OpenPyXL: Excel file processing
python-docx: Word document processing
Pillow: Image processing
pytesseract: OCR capabilities

Database & Storage

Firebase Firestore: NoSQL database
Firebase Authentication: User management
Local File System: File storage

DevOps & Deployment

Docker: Containerization
Docker Compose: Multi-container orchestration
Nginx: Reverse proxy and static file serving
Gunicorn: WSGI HTTP server

Development Tools

pytest: Testing framework
black: Code formatting
flake8: Linting
isort: Import sorting

Team

Ayush Addhyayan – GitHub | LinkedIn
Sriram B – GitHub | LinkedIn
Abhiram Bhimavarapu – GitHub | LinkedIn

Sample Dataset

Startup Evaluation Report Structure

The AI generates comprehensive reports with the following structure:

{
  "startupId": "strp_001",
  "submission": {
    "submittedBy": "founder@example.com",
    "submittedAt": "2024-01-15T10:30:00Z",
    "startupName": "Example Startup",
    "location": {
      "city": "Bangalore",
      "state": "Karnataka", 
      "country": "India"
    },
    "foundingDate": "2023-01-01",
    "founderIds": ["user_123"],
    "uploadedAssets": [...]
  },
  "companyProfile": {
    "description": "AI-powered solution...",
    "sector": "Fintech",
    "businessModel": "B2B SaaS",
    "companyStage": "Seed",
    "teamSize": 15
  },
  "founderProfiles": [...],
  "teamStructure": {...},
  "product": {...},
  "traction": {...},
  "market": {...},
  "competitorLandscape": {...},
  "financials": {...},
  "aiInsights": {...},
  "scores": {
    "FounderMarketFit": 8.6,
    "ProductDifferentiation": 8.3,
    "GoToMarketStrategy": 7.9,
    "CXScore": 9.2,
    "Traction": 8.1,
    "FinancialHealth": 7.2,
    "TeamQuality": 8.0,
    "MarketPotential": 9.0,
    "RiskAdjustedScore": 7.8,
    "OverallScore": 8.4
  },
  "agentPipeline": [...],
  "timestamps": {...},
  "version": "1.1"
}

Key Metrics Analyzed

Financial Metrics: Revenue, burn rate, runway, funding requirements
Traction Metrics: User growth, retention, engagement, market penetration
Team Metrics: Founder experience, team composition, advisor network
Market Metrics: TAM/SAM/SOM, growth rates, competitive positioning
Product Metrics: Maturity, differentiation, roadmap, technical capabilities

AI Features

Multimodal Processing

Document Analysis: PDF pitch decks, financial models, business plans
Video Processing: Pitch videos, product demos, founder presentations
Audio Analysis: Audio pitches, interview recordings
Image Processing: Screenshots, diagrams, infographics

Intelligent Analysis

Automated Scoring: 10+ dimensional scoring system
Risk Assessment: Automated risk identification and flagging
Market Analysis: TAM/SAM/SOM calculations and growth projections
Competitive Analysis: Competitor identification and positioning
Financial Health: Burn rate, runway, and funding analysis

Report Generation

Structured Output: Standardized JSON format with 100+ metrics
Deal Memos: Automated investment memo generation
Visualizations: Charts and graphs for key metrics
Recommendations: AI-generated next steps and insights

Submission Deliverables

Core Platform Features

Multi-role Authentication: Founder, Investor, and Admin dashboards
AI-Powered Analysis: Gemini AI integration for startup evaluation
File Upload System: Support for multiple file types and formats
Real-time Processing: Asynchronous AI processing with status updates
Comprehensive Reporting: 100+ metric evaluation system

Technical Implementation

Flask Backend: RESTful API with modular architecture
Firebase Integration: Authentication and Firestore database
Docker Containerization: Complete containerization setup
Responsive UI: Clean, intuitive interface optimized for all devices
Security Features: Input validation, CSRF protection, role-based access

AI & ML Capabilities

Multimodal Processing: Text, image, video, and document analysis
Intelligent Scoring: Comprehensive evaluation metrics
Risk Assessment: Automated risk identification
Market Analysis: TAM/SAM/SOM calculations
Deal Memo Generation: Automated investment documentation

Documentation & Deployment

Comprehensive README: Detailed setup and usage instructions
API Documentation: Complete endpoint documentation
Docker Configuration: Production-ready containerization
Environment Setup: Complete configuration management
Architecture Diagrams: Mermaid-based system documentation

Testing & Quality

Unit Tests: Comprehensive test coverage
Code Quality: Linting, formatting, and best practices
Error Handling: Robust error management and logging
Performance Optimization: Efficient data processing and storage

API Documentation

Authentication Endpoints

POST /auth/api/login - User login
POST /auth/api/signup - User registration
POST /auth/api/verify-token - Verify Firebase token
GET /auth/logout - User logout

Founder Endpoints

GET /founder/dashboard - Founder dashboard
GET /founder/profile - Founder profile
GET /founder/startups - Manage startups
POST /founder/api/startup - Create startup submission
PUT /founder/api/startup/<id> - Update startup
DELETE /founder/api/startup/<id> - Delete startup

Investor Endpoints

GET /investor/dashboard - Investor dashboard
GET /investor/profile - Investor profile
GET /investor/startups - Browse startups
GET /investor/investments - View investments
POST /investor/api/invest - Create investment
GET /investor/startup/<id> - View detailed startup analysis

Admin Endpoints

GET /admin/dashboard - Admin dashboard
GET /admin/users - User management
GET /admin/startups - Startup management
GET /admin/investments - Investment management
PUT /admin/api/user/<id>/status - Update user status

Security Features

Input Validation: Comprehensive validation and sanitization
Role-based Access Control: Different access levels for different user types
CSRF Protection: Built-in CSRF protection for forms
Secure Sessions: HTTP-only, secure session cookies
Password Requirements: Strong password validation
XSS Protection: Input sanitization and output encoding
File Upload Security: Type validation and size limits

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support and questions:

Create an issue in the repository
Check the documentation
Review the logs for error details
Contact the development team

Changelog

v2.0.0 - AI Integration & UI Overhaul

Complete AI integration with Gemini API
Multimodal file processing capabilities
Comprehensive scoring and analysis system
Clean, intuitive interface with modern design principles
Real-time processing pipeline
Enhanced user experience and animations

v1.0.0 - Initial Release

Role-based authentication system
Firebase integration
Basic startup submission functionality
Investor dashboard
Admin panel
File upload system

Built with love by the Aicurate Team

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
Demo		Demo
blueprints		blueprints
config		config
images		images
models		models
services		services
static		static
templates		templates
tests		tests
uploads		uploads
utils		utils
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
Makefile		Makefile
README.md		README.md
SUBMISSION_FEATURES.md		SUBMISSION_FEATURES.md
__init__.py		__init__.py
app.py		app.py
app_factory.py		app_factory.py
docker-compose.dev.yml		docker-compose.dev.yml
docker-compose.yml		docker-compose.yml
env.production.example		env.production.example
env_example.txt		env_example.txt
generate_startup_report.py		generate_startup_report.py
nginx.conf		nginx.conf
requirements.txt		requirements.txt
run.py		run.py
test_setup.py		test_setup.py
wsgi.py		wsgi.py

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