FP&A Command Center

Local-first AI FP&A command center for planning, forecasting, variance analysis, financial statements, CSV uploads, live data, templates, and exports—no login or backend needed.

Comment

About the Project

FP&A Command Center AI Ultra is a local-first Financial Planning & Analysis platform built to make finance planning, forecasting, and analysis easier to understand and execute without needing an enterprise EPM system, backend server, login, or external API.

The inspiration came from a practical problem: most FP&A tools are either too complex, too expensive, or too dependent on enterprise infrastructure. Small teams, students, consultants, founders, and finance learners often need a hands-on planning environment where they can upload data, test assumptions, forecast performance, analyze variances, and understand financial statements without waiting for a full corporate implementation.

This project was built to bridge that gap.

What It Does

FP&A Command Center AI Ultra provides a complete local-first FP&A workflow:

  • Demo Mode with sample financial data
  • Live Mode for user-uploaded or manually entered data
  • CSV upload and mapping
  • Template library for FP&A data inputs
  • Editable planning grids
  • Revenue, OPEX, Headcount, CAPEX, and Working Capital planning
  • P&L, Balance Sheet, and 3-Statement model
  • Forecasting and variance analysis
  • Anomaly detection
  • Export functionality
  • Browser-based persistence using local storage
  • AI Finance Copilot prompt support for finance analysis and commentary

The app is designed as a Release Candidate for lightweight FP&A workflows, learning, hackathon evaluation, and consulting demos.

What Inspired Me

The project was inspired by the gap between spreadsheets and enterprise FP&A platforms.

Spreadsheets are flexible but fragile. Enterprise tools like Anaplan, Oracle EPM, OneStream, or Workday Adaptive Planning are powerful but expensive and implementation-heavy. I wanted to build something in between:

A lightweight FP&A command center that feels like an enterprise finance planning system but works locally in the browser.

The goal was not to replace enterprise EPM systems, but to create a practical and educational FP&A tool that helps users understand the full finance workflow from input data to management insights.

How I Built It

The app was built as a local-first browser application.

The architecture uses:

  • Local browser storage for persistence
  • Demo Mode and Live Mode separation
  • CSV upload and mapping flow
  • Normalized financial fact records
  • Editable planning grids
  • Calculation logic for financial statements
  • Forecasting formulas
  • Variance calculations
  • Anomaly rules
  • Export utilities
  • Template-driven data onboarding

Forecasting includes methods such as naive forecast, moving average, weighted moving average, trend-based forecast, and ensemble-style forecast.

Key Features

Demo Mode

Demo Mode allows users to explore the platform immediately with sample FP&A data. It helps users understand how dashboards, statements, forecasts, and variance analysis work before uploading their own data.

Live Mode

Live Mode allows users to work with their own data. Users can manually enter data or upload CSV files. Live Mode is separated from Demo Mode so user data does not overwrite sample data.

Upload and Mapping Center

The Upload Center supports structured CSV workflows. The intended flow is:

  1. Select upload type
  2. Download template
  3. Upload CSV
  4. Map fields
  5. Validate data
  6. Import into Live Mode
  7. Review dashboard and statement impact

Template Library

The app includes a template library covering FP&A data types such as revenue planning, OPEX, CAPEX, working capital, balance sheet, budget vs actual, forecast, trial balance, general ledger, KPI, profitability, company setup, assumptions, and AI Finance Copilot prompts.

Financial Statements

The app includes:

  • P&L Statement
  • Balance Sheet
  • 3-Statement Model
  • Cash Flow logic

These help users understand how planning inputs flow into financial outputs.

AI Finance Copilot Support

The AI Finance Copilot section is designed to help users structure financial analysis prompts. It supports use cases like:

  • Variance explanation
  • CFO summary
  • Forecast commentary
  • Cash risk review
  • OPEX challenge
  • Revenue driver analysis
  • Board-pack narrative
  • Anomaly explanation
  • Scenario recommendation
  • Management action planning

Challenges I Faced

The biggest challenge was not creating pages. The real challenge was making the workflows honest and connected.

Some of the key issues discovered during testing included:

  • Live Mode accidentally showing demo data
  • Generic upload validation being applied to specific templates
  • Required fields like account and value appearing for templates that should not need them
  • Incorrect column mapping such as Discount_Percent mapping to units
  • Uploaded product and customer fields not always appearing in downstream views
  • Duplicate facts being created when data was saved or uploaded repeatedly
  • Export files needing clear Demo/Live mode labels
  • Some configuration templates needing to be clearly marked as manual-reference templates

These issues forced the project to move from surface-level testing to input-output testing. Every important workflow had to be checked using:

Input → Expected Output → Actual Output → Difference → Fix → Retest

Built With

  • medo
Share this project:

Updates