AI-Native CFO Operating System

An interactive CFO platform that combines spend intelligence, cashflow forecasting, agentic decisioning, FP&A automation, compliance workflows, and executive narration in a single Streamlit app.

Team Name : Due Diligence CFO-OS Name : Finvigators

Demo Link: https://drive.google.com/file/d/1nJL_MafmOhklghvLmw1x45D_GOqk-Hnz/view?usp=sharing

Website Link : https://finvigators-zagglexcomet26.streamlit.app

Subscription Pricing Model : https://docs.google.com/spreadsheets/d/1fUf2hL04sLCcuBkh9YWXB8wuhGxy6WOfPxoHBJXy3AE/edit?usp=sharing

Feature Explanation : https://docs.google.com/document/d/17ryeeImuRTrx7Mbb1Jeq4fHatHzXlNZRUNuhOeALfDQ/edit?usp=sharing

Formulae Guide : https://docs.google.com/document/d/13iqnZHiNpK90wVeyXGlOUb0JukfqBdSVSM4y3yb-Imk/edit?usp=sharing

Presentation(11-04-26) link : https://drive.google.com/file/d/1DtbcYVBu7wab-QSAb57ko9fJus5nPMJo/view?usp=drivesdk

System Flow

Zaggle Transaction Exports -> Feature Engineering -> Inference Models -> Agentic Orchestrator
                     -> FP&A / Compliance Workflows -> Generative Narration -> CFO UI

Product Layout

┌─────────────────────────────────────────────────────────────────────────────────────┐
│                              STREAMLIT UI (app.py)                                 │
│  Alert Dashboard | Agent Reasoning | CFO Briefing | FP&A Workbench                 │
│  Overview | Compliance & Close | Strategic Planning                                │
└───────────────────────────────────┬─────────────────────────────────────────────────┘
                                    │
                                    ↓
┌─────────────────────────────────────────────────────────────────────────────────────┐
│                        ORCHESTRATOR (orchestrator.py)                               │
│  Scenario inputs -> data -> features -> inference -> agents -> domain workflows    │
└──────────────┬─────────────────┬─────────────────┬──────────────────┬──────────────┘
               ↓                 ↓                 ↓                  ↓
         ┌──────────┐      ┌──────────┐      ┌──────────┐      ┌────────────┐
         │ data.py  │      │features.py│     │inference.py│     │ agents/*.py │
         └──────────┘      └──────────┘      └──────────┘      └────────────┘

Current Modules

UI (app.py)

• 🚨 Alert Dashboard
• 🤖 Agent Reasoning
• 📋 CFO Briefing
• 📐 FP&A Workbench
• 🧭 Overview
• 🧾 Compliance & Close
• 🗺️ Strategic Planning

Data and Features

• data.py: scenario-aware transaction and payment generation
• features.py: burn, growth, budget, and anomaly-oriented feature engineering

Inference

• inference.py: anomaly detection, forecasting, Monte Carlo simulation, ARIMA stress support

Agents

• SpendIntelligenceAgent
• CashFlowForecastAgent
• DecisionAgent
• NarrativeAgent

Supporting Layers

• orchestrator.py: pipeline coordination and output assembly
• memory.py: anomaly and decision history
• evaluation.py: quality and health tracking

Scenario Inputs

Primary controls:

• Sector
• Business Scale
• Macro Environment
• Close Pressure
• Automation Maturity
• Current Cash Balance

Advanced assumptions:

• Forecast Horizon (days)
• Burn Shock (%)
• Collections Delay (days)
• Monte Carlo Sims
• Revenue Outlook (%)
• Hiring Growth (%)
• Working Capital Efficiency (%)

These inputs are connected to the runtime pipeline, so outputs change across iterations.

Core Capabilities

Real-Time Spend Intelligence

• anomaly detection over scenario-aware transaction data
• category-level overspend identification
• severity and confidence scoring

Cashflow Forecasting and Optimization

• liquidity-aware burn modeling
• projected ending cash and runway analysis
• deterministic Monte Carlo simulation
• ARIMA stress testing
• baseline vs recommended-action comparison

FP&A Automation

• budgeting and variance analysis
• forecasting and performance tracking
• scenario modeling
• planning narration
• sensitivity analysis

Compliance and Close

• exception queues
• reconciliation queues
• auto-match metrics
• review and escalation counts
• close risk scoring

Strategic Planning

• links revenue, hiring, working-capital, and automation assumptions to outcomes

Advanced Expansion Roadmap

The following capability groups would make the platform materially stronger as a broader CFO operating system. They should be treated as roadmap modules unless explicitly implemented in code.

Group I — FP&A

• driver-based forecasting with DAG propagation
• BvA variance with price-volume-mix decomposition and LLM narration
• KPI benchmarking against peer sets
• cohort survival and LTV modeling with BG/NBD and Gamma-Gamma

Group II — Risk and Scenarios

• tornado and spider-chart sensitivity analysis
• elasticity coefficients across key drivers
• correlated Monte Carlo using a Cholesky structure
• forward and reverse stress testing
• scenario decision trees
• corporate VaR, CVaR, EaR, and CFaR

Group III — Treasury and Cash

• 13-week direct-method rolling forecast
• genetic-algorithm cash optimization
• working-capital liberation modeling from CCC -> cash
• capital structure and WACC optimization
• FX and commodity hedge programme management

Group IV — ML and Intelligence

• Isolation Forest plus autoencoder anomaly detection
• budget consumption rates and HHI vendor concentration
• LSTM and ensemble ML cash forecasting
• ML-augmented revenue forecasting with churn prediction

Group V — Strategy and Valuation

• multi-method DCF / LBO / comps valuation with football-field output
• ROIC, EVA, and real-options capital allocation
• full M&A accretion-dilution and synergy modeling

Group VI — Close and Compliance

• auto-reconciliation plus journal-entry risk scoring
• tax provision automation with ETR bridge and Pillar Two impact modeling
• a master CFO Decision Synthesis Engine that aggregates upstream engines into a prioritized decision brief with confidence-scored reasoning chains

Decision Engine

The recommendation layer simulates action-level combinations and scores them using a CFO-style trade-off function:

score = (
    0.5 * risk_reduction
    - 0.2 * business_cost
    + 0.2 * feasibility
    + 0.1 * reversibility
)

Base actions:

• cut_marketing
• delay_vendor
• reduce_discretionary
• do_nothing

Scenario-specific actions may also include:

• optimize_cloud
• freeze_hiring
• rebalance_inventory
• tighten_promotions
• optimize_staffing_mix
• tighten_procurement
• defer_capex
• consolidate_vendors

The engine returns:

• best action
• recommended level
• confidence based on the score gap
• top comparison set
• available actions for the current scenario
• recommended-action cashflow simulation

Quick Start

Install

cd /Users/hemang/Desktop/cfo_os
pip install -r requirements.txt

Launch the UI

streamlit run APP_PATH

Then open http://localhost:8501.

Run CLI Analysis

python3 orchestrator.py

Run Integration Test

python3 test_system.py

Example Usage

from orchestrator import CFOOrchestrator

orchestrator = CFOOrchestrator(
    current_cash=180000,
    sector="retail",
    business_scale="enterprise",
    macro_environment="inflationary",
    close_pressure="high",
    automation_maturity="medium",
    planning_assumptions={
        "forecast_horizon_days": 60,
        "burn_shock_pct": 0.10,
        "collections_delay_days": 5,
        "monte_carlo_sims": 400,
        "revenue_outlook_pct": -0.05,
        "hiring_growth_pct": 0.05,
        "working_capital_efficiency": 0.10,
    },
)

result = orchestrator.run_analysis()

Output Domains

run_analysis() returns:

• spend_intelligence
• cashflow_forecast
• decision_analysis
• executive_briefing
• fpa_analysis
• compliance_analysis

It also includes scenario metadata such as sector, scale, macro environment, close pressure, automation maturity, and planning assumptions.

Related Docs

• ARCHITECTURE.md
• QUICKSTART.md
• DEPLOYMENT.md