AI-DRIVEN ACCOUNTING PLATFORM WITH REAL-TIME DASHBOARD

Objective.
Develop a full accounting platform with an interactive dashboard that provides real-time profit and liability tracking, financial analysis (including inventory turnover), and AI-driven insights.
The platform should offer core accounting functionalities, be cloud-hosted, and scalable for multiple users/companies.
Core Features.
1.
Accounting Suite Features.
General Ledger.
Record all financial transactions in a double-entry system.
Accounts Payable/Receivable.
Track vendor payments and customer invoices.
Expense Tracking.
Record business expenses and categorize them for reporting.
Invoicing.
Automated invoicing and recurring invoices.
Multi-currency support.
Bank Reconciliation.
Reconcile bank statements with transaction records.
Tax Management.
Automatic tax calculation (VAT, GST, etc.).
Integration with local tax systems for compliance.
Payroll Management.
Support for employee payments, tax deductions, and benefits management.
Inventory Management.
Real-time tracking of stock levels.
Automatic stock reordering notifications.
Inventory turnover rate calculation.
Financial Reports.
Profit and Loss (P&L), balance sheet, cash flow statements.
Customizable reports based on date ranges, product categories, etc.
Real-Time Dashboard Requirements.
1.
General Overview.
A dashboard that visualizes the overall financial health of the business in real time.
Displays essential KPIs such as.
Real-time profits (overall and per product).
Liabilities (payables and other debts).
Inventory turnover.
Highlight which products are moving fast and which are underperforming.
Expense analysis.
Break down of expenses by category (e.
., operational, marketing, etc.).
Cash flow trends.
Current cash inflows and outflows.
Accounts receivable/payable balances.
2.
Specific Components.
Real-time Profit Analysis.
Profit breakdown by product/service, updated dynamically.
Filters for time periods (daily, weekly, monthly).
Alerts for products or services that are performing below expected profitability.
Liability Tracking.
Track total liabilities, including short-term debts, accounts payable, and long-term obligations.
Visualize upcoming payments and their due dates.
Inventory Turnover.
Show inventory turnover ratio and aging of inventory items.
Identify fast-moving and slow-moving stock.
Predict future inventory needs using AI-driven forecasting.
3.
Customization.
Allow users to customize which KPIs are visible on the dashboard.
Custom filters for date ranges, specific products/services, or geographic regions.
Export options (PDF, CSV) for dashboard reports.
4.
Technology Stack for Dashboard.
Frontend.
Use React.
s or Vue.
s for building the user interface.
Integrate visual libraries like [login to view URL], Plotly, or D3.
s for interactive visualizations.
Backend.
Implement real-time data fetching via REST APIs or GraphQL.
For real-time updates, use WebSockets or Server-Sent Events (SSE).
Database.
Use PostgreSQL or MySQL to store financial data.
Use Redis for caching and fast access to frequently queried data.
AI Integration Requirements.
1.
Predictive Analytics.
AI models should predict.
Future profits based on historical data and trends.
Cash flow predictions for the next , and 90 days.
Inventory demand forecasting based on sales data and seasonal trends.
Technology.
Integrate TensorFlow, PyTorch, or Scikit-learn for machine learning.
2.
Automated Categorization.
Use NLP to categorize and classify financial transactions (e.
., automatically classify expenses).
Suggest categorization for uncategorized transactions based on historical patterns.
3.
Anomaly Detection.
Detect anomalies in transactions (e.
., fraudulent or unusual transactions) and flag them for review.
Create alerts for unusual spikes in expenses or cash outflows.
Platform and Deployment Requirements.
1.
Cloud Hosting.
Host the platform on a cloud provider like AWS, Google Cloud, or DigitalOcean.
Ensure scalability for multiple users/companies.
Implement containerization using Docker for easy deployment and scaling.
Option to host the system as SaaS with multi-tenant architecture (separate data instances for each client).
2.
Security.
Implement SSL/TLS encryption for secure access (HTTPS).
Role-based access control for different users (admin, accountant, auditor).
Data isolation between different clients (for SaaS model).
Two-factor authentication (2FA) for login.
3.
Integration with Other Tools.
Integration with payment gateways (Stripe, PayPal, etc.) for processing payments.
Integration with bank APIs for automatic bank statement imports.
REST API or GraphQL API for third-party integrations and custom workflows.
4.
Performance and Optimization.
Ensure real-time data fetching for dashboard without significant delays.
Optimize database queries to handle large volumes of transactional data efficiently.
Testing and Deployment.
1.
Testing.
Perform unit testing on all major features (accounting calculations, inventory tracking).
Stress test the dashboard to ensure it can handle high traffic and real-time updates.
Test the AI models for accuracy and real-time predictions.
2.
Deployment.
Deploy to a production environment on a cloud provider.
Set up CI/CD pipelines for continuous integration and updates.
3.
Documentation.
Provide user documentation for clients on how to use the platform and dashboard.
Developer documentation for future scaling and feature additions.
Timeline & Milestones.
Phase 1.
Set up basic accounting functionalities (ledger, invoicing, expense tracking).
Phase 2.
Develop and integrate the real-time dashboard.
Phase 3.
Implement AI-driven features (predictive analytics, anomaly detection).
Phase 4.
Final testing, deployment, and user onboarding MySQL NLP Machine Learning (ML) Python HTTP ID do Projeto.
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