FULL STACK FINANCE CONSULTANCY

Financial intelligence,
built like software.

Python operating models. AI-native BI. Machine learning drivers. From clean books to board decks—the entire stack, owned.

Schedule a Discovery Call See the Stack →
arr_forecast.py
# Bottoms-up ARR forecast with cohort logic
import pandas as pd
from pythia import CohortEngine, ChurnModel

class ARRForecast:
  def __init__(self, contracts_df):
    self.cohorts = CohortEngine(contracts_df)
    self.churn = ChurnModel()

  def forecast(self, months=12):
    base = self.cohorts.prorate_mrr()
    expansion = self.model_ndr(base)
    churn_adj = self.churn.predict(base)
    return base + expansion - churn_adj
// The Full Stack
Own the entire financial stack.

Most consultancies pick a lane. We integrated all three layers—because insights without clean data are fiction, and data without intelligence is noise.

LAYER 03
◎

Intelligence

ML drivers, Python models, AI-native BI. Forecasts that learn.

Python Evidence Claude API scikit-learn
LAYER 02
⟁

Analysis

FP&A, forecasting, scenario models. Strategic insight from clean data.

dbt SQL Pandas DuckDB
LAYER 01
⊞

Foundation

Clean books, payroll, tax, compliance. The unglamorous work that makes everything else possible.

QBO Gusto Bill.com Ramp
// Data Pipeline
Messy data in, clean insight out.

Every stage version-controlled, tested, and reproducible. No broken spreadsheets. No manual refresh.

⊞
01 — INGEST
Source Systems

Extract from QuickBooks, CRMs, payroll systems, and banks via automated connectors.

QuickBooks Stripe REST APIs
⟁
02 — TRANSFORM
Python Models

Bottoms-up operating models with proration logic, cohort analysis, and scenario engines.

Python Pandas dbt
◎
03 — ENRICH
ML Drivers

Churn prediction, revenue driver analysis, and anomaly detection on financial data.

scikit-learn XGBoost Claude API
◧
04 — DELIVER
AI-Native BI

Code-based dashboards that version control like software and update with every commit.

Evidence Rill Streamlit
// Services
The full offering.

A one-person consultancy that delivers like an in-house team. Flexible engagement, full-stack capability.

pythia --services
$ pythia list --category=foundation
▸ Bookkeeping — Monthly close, reconciliation, clean books that scale
▸ Payroll — Processing, compliance, state registrations
▸ Tax Prep — Coordination with your CPA, estimated payments, 1099s
▸ AP/AR — Bill pay, invoicing, collections, cash management
$ pythia list --category=analysis
▸ FP&A — Budgeting, forecasting, variance analysis
▸ Scenario Modeling — What-if analysis, sensitivity testing
▸ Board Decks — Investor-ready financials and KPI packages
▸ Due Diligence — M&A support, data room prep, QofE assist
$ pythia list --category=intelligence
▸ Custom Dashboards — Evidence/Rill BI, real-time metrics
▸ Python Models — ARR waterfalls, cohort analysis, revenue drivers
▸ ML Forecasting — Churn prediction, demand forecasting
▸ Automation — Report generation, data pipelines, integrations
$ _
// Why This Is Different
Traditional vs. Pythia

Most finance consultancies hand you a spreadsheet and a slide deck. We deliver production-grade infrastructure.

📊

Traditional Approach

  • ✕ Excel models with broken formulas
  • ✕ Static reports that age instantly
  • ✕ Manual data refresh every month
  • ✕ "Trust me" variance explanations
  • ✕ Consultant-dependent knowledge
  • ✕ Separate vendors for books vs BI
⚡

Pythia Approach

  • ✓ Python models, version-controlled
  • ✓ Live dashboards that auto-refresh
  • ✓ Automated data pipelines
  • ✓ Traceable, auditable transformations
  • ✓ Documented infrastructure you own
  • ✓ Full stack from one consultant
01

Code > Spreadsheets

Operating models built in Python, not Excel. Version-controlled, testable, auditable. No broken formulas, no hidden tabs.

02

AI-Augmented Analysis

LLMs integrated directly into workflows—automated variance commentary, intelligent data quality checks, natural language querying.

03

Real-Time BI

Dashboards built as code with Evidence and Rill. They deploy like software, refresh automatically, and scale without manual work.

04

Finance-Native

Built by someone who's lived in FP&A—ARR waterfalls, commission plans, board decks, due diligence. The tech serves the finance.

// Interactive
See it work.

A sample ARR scenario model. Adjust the inputs and watch the outputs recalculate in real time.

LIVE
$12.0M
112%
25%
8%
12-MONTH PROJECTION
Ending ARR $15.5M
Net New ARR +$3.5M
Expansion Revenue +$1.4M
Churned ARR -$1.0M
Implied ARR Growth 29.2%
RULE OF 40
37.2

Ready to upgrade your
financial infrastructure?

Let's talk about what code-native finance can do for your business.

Schedule a Discovery Call