S21G
Blueprint Library
Finance

The Financial Clarity Engine

Gives you real-time financial visibility without a full-time CFO

Trigger
AI Agent
Human Review
Output

How It Works

All transactions flow through an AI categorization layer that tags spend by department, project, and category based on rules you define. A monitoring system tracks performance against your targets: cash runway, gross margin, burn rate, and key expense categories. When a metric drifts outside expected ranges or an anomaly is detected, a flagged report surfaces for owner review. Weekly and monthly summaries generate automatically.

Step-by-Step Flow

1

Connect your accounting system and bank feeds

2

Define categorization rules and KPI targets

3

AI categorizes transactions and calculates metrics continuously

4

Anomalies and threshold breaches surface as alerts

5

Owner reviews flagged items and makes decisions

6

Approved changes logged, weekly summary auto-generated

Best For

  • Founder-led businesses spending 5+ hours/week on financial reporting
  • Companies that want CFO-level visibility without CFO-level cost
  • Businesses growing fast enough that manual tracking is breaking down

This is customized for your business.

Every node, tool, and logic path shown here gets adapted to your team structure, your CRM, and your existing workflows. What you see is the proven pattern. What we build together is built specifically for you.

Implementation Notes

Connects to accounting systems via API: QuickBooks Online, Xero, and FreshBooks are natively supported. Bank feeds are pulled via Plaid or directly from bank CSV exports when API access is unavailable. Transaction categorization uses a rule-based system with LLM fallback for ambiguous items: known vendor names are rule-matched, new or unusual vendors are classified by an LLM that reviews payee name, amount, and surrounding transaction context. Categories map to your chart of accounts. KPIs tracked include cash runway at current burn rate, gross margin by department or project if data is available, month-over-month expense variance by category, and accounts receivable aging. Anomaly detection flags: any single transaction above a configurable threshold (typically twice the category rolling average), any category exceeding its monthly budget by more than 15 percent, and any week where cash balance drops more than 20 percent from the prior week. Alerts deliver via email or Slack with a plain-language summary and a direct link to the flagged line items. Weekly summaries auto-generate in Google Docs or Notion in a consistent template. Monthly close typically requires 30 minutes of owner review versus the prior 3 to 5 hours. Prerequisites: an accounting system with API access and at least six months of historical transaction data.