FP&A Platform

Part of the worlds-biggest-software-project initiative.

An open-source, AI-native financial planning and analysis platform for driver-based modelling, budgeting, and scenario planning.

FP&A Platform is a multi-dimensional planning engine for finance teams who need to model business drivers, run scenarios, and reforecast continuously without paying six-figure contracts to incumbent vendors. It is aimed at mid-market CFOs, enterprise FP&A leaders, and SaaS finance teams who want transparent, auditable models instead of black-box proprietary tools. The project addresses a market that today has no viable open-source alternative.

Why FP&A Platform?

Every established FP&A tool — Anaplan, Workday Adaptive, Planful, Vena, Pigment — is fully proprietary, with no viable open-source planning engine in the category.
Pricing locks out a large underserved segment: minimum viable Anaplan implementations exceed $100K/year, Planful starts at ~$32K/year, and most platforms require multi-month sales cycles before disclosing a quote.
Companies between $5M–$50M revenue cannot justify these contracts and are forced to remain on Excel-based management packs that lack governance, version control, and audit trails.
Existing AI features sit inside opaque commercial platforms; finance and audit functions need transparent, inspectable driver logic with version-controlled change history before they can adopt AI in regulated environments.
Anaplan's Hyperblock architecture is proprietary, but the underlying multi-dimensional MOLAP techniques, driver-based modelling, and three-statement financial modelling are standard, non-patented approaches — making an OSS engine viable.

Key Features

Planning Engine and Financial Model

Multi-dimensional planning engine with driver-based modelling and formula evaluation
Three-statement financial model (P&L, balance sheet, cash flow) with automatic linkage
Rolling forecast automation with configurable time horizons
Scenario management with at least three concurrent plan versions and side-by-side variance comparison
Workforce and headcount planning with compensation roll-up to P&L

Consolidation, Actuals, and Workflow

Multi-entity consolidation with currency translation and intercompany eliminations
ERP and accounting integration for automated actuals loading (QuickBooks, Xero, NetSuite at minimum)
Continuous actuals sync with intra-month reforecast recalibration
Role-based access for budget owners, FP&A analysts, CFOs, and read-only stakeholders
Approval workflows for budget submission and reforecast sign-off

AI Agents and Natural-Language Interface

Natural-language query interface for ad-hoc variance and forecast questions
AI-generated variance commentary for management reporting and board packs
Conversational scenario generation that turns plain-English prompts into multi-dimensional plan branches
Automated driver discovery surfacing statistical correlations between operational metrics and financial outcomes
Anomaly detection on actuals to flag unexpected transactions or coding errors before period close

SaaS and Specialised Modules

SaaS metrics module covering ARR, MRR, NRR, churn, CAC, and LTV cohort analysis
13-week cash flow forecasting with bank feed integration
Board pack export templates with auto-populated charts and commentary
XBRL export for public company regulatory submissions

AI-Native Advantage

Incumbent platforms bolt AI onto proprietary engines users cannot inspect. This project is designed AI-native around four capabilities the source research identifies as genuinely differentiating: automated driver discovery that proposes and validates correlations between operational and financial data, natural-language scenario generation that lowers the skill barrier for non-finance stakeholders, continuous forecast recalibration from live ERP and billing feeds rather than monthly batch cycles, and explainable AI guardrails with transparent driver logic and version-controlled model history suitable for audit and regulated finance environments.

Tech Stack & Deployment

The platform is intended to ship as an open-source core with an optional hosted cloud tier, supporting both self-hosted and managed deployments. The planning engine is built on standard multi-dimensional data modelling techniques — OLAP cubes, columnar storage, and DAG-based dependency resolution — with no identified IP risk. Outputs are mappable to XBRL taxonomies for SEC and regulatory submissions, and produce GAAP- and IFRS-compliant financial statements. Integration follows the de facto REST + JSON pattern, with pre-built connectors planned for the major ERP, HRIS, and accounting systems referenced in the source research.

Market Context

The global FP&A software market was valued at approximately $5.82B in 2024 and is projected to reach $13.91B by 2033 at a CAGR of ~10.2% (Data Insights Market). Cloud-based FP&A for public companies is projected at ~$8.5B in 2026 growing at 28% CAGR (MGI Research), with the AI-powered FP&A segment growing 16–18% annually through 2034. Primary buyers are mid-market CFOs replacing Excel, enterprise FP&A directors needing multi-entity consolidation, SaaS finance leaders requiring ARR and cohort metrics, and startup CFOs graduating from spreadsheets — segments currently paying $24K to $1M+ per year to proprietary vendors.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.