Finta Local Dev Loop

Overview

Finta is primarily UI-driven without a public API. For local automation, use CSV exports from Finta combined with Python scripts for analysis, reporting, and integration with other tools.

Instructions

Export Pipeline Data

1. In Finta, go to Pipeline > Export > CSV
2. Save as

   pipeline-export.csv

Analyze Fundraise Pipeline

import pandas as pd
from datetime import datetime

# Load Finta export
df = pd.read_csv("pipeline-export.csv")

# Pipeline summary
summary = df.groupby("Stage").agg(
    count=("Name", "count"),
    avg_check=("Check Size", "mean"),
).reset_index()

print("Pipeline Summary:")
print(summary.to_string(index=False))

# Conversion rates
stages = ["Researching", "Reaching Out", "Intro Meeting", "Follow-up", "Due Diligence", "Term Sheet", "Closed"]
for i in range(len(stages) - 1):
    current = len(df[df["Stage"] == stages[i]])
    next_stage = len(df[df["Stage"] == stages[i+1]])
    rate = (next_stage / current * 100) if current > 0 else 0
    print(f"  {stages[i]} -> {stages[i+1]}: {rate:.0f}%")

Weekly Pipeline Report

def generate_weekly_report(df: pd.DataFrame) -> str:
    total = len(df)
    active = len(df[df["Stage"].isin(["Intro Meeting", "Follow-up", "Due Diligence"])])
    term_sheets = len(df[df["Stage"] == "Term Sheet"])
    closed = len(df[df["Stage"] == "Closed"])

    return f"""
Fundraise Pipeline Report ({datetime.now().strftime('%Y-%m-%d')})
==================================================
Total investors: {total}
Active conversations: {active}
Term sheets: {term_sheets}
Closed: {closed}
"""

Resources

• Finta Website

Next Steps

See

finta-sdk-patterns

for integration patterns.