Before you put AI on the finance function, fix the data it will rely on.
I help PE-backed software companies build SaaS metrics that reconcile to source data, withstand board and buyer scrutiny, and connect to Claude and ChatGPT through a trusted metrics layer.
AI can summarize a bad number just as confidently as a good one.
Most software finance teams do not have a formula problem. They have a data foundation problem. Adding AI before fixing that foundation can accelerate reporting without improving the reliability of the answer.
ARR movement does not tie out
New, expansion, contraction, churn, reactivation, and ending ARR are assembled from disconnected files.
Retention is hard to reproduce
GRR and NRR depend on fragile spreadsheet logic, incomplete customer histories, or changing definitions.
Margins shift with the coding
COGS, OpEx, payroll, contractors, and AI costs are not consistently mapped to the functions delivering the revenue.
The board pack is rebuilt every month
Finance spends its time gathering and checking data instead of explaining performance and helping leaders act.
Separate the trusted calculation layer from the AI communication layer.
Claude and ChatGPT are powerful for analysis, narrative, board updates, investor reporting, and scenario questions. But the underlying metrics should be calculated from structured source data with documented logic.
Four data sources power an accurate SaaS metrics operating system.
The work starts before the dashboard. Each source must be gathered, mapped, normalized, and connected to a repeatable monthly process.
Financial data
Chart of accounts, SaaS P&L mapping, revenue streams, COGS vs. OpEx, departments, and AI cost structure.
Bookings data
New ARR, expansion, services, downgrades, booking dates, revenue types, and GTM attribution.
People data
Payroll, contractors, FTEs, department mapping, fully burdened costs, Rev/FTE, ROSE, and operating leverage.
Customer and revenue data
Invoices, subscriptions, MRR schedules, customer counts, churn, expansion, contraction, retention, and ARR movement.
One finance data foundation. Four high-stakes uses.
Management can operate from it
A repeatable monthly process turns finance data into an operating rhythm, not a recurring fire drill.
The board can trust it
Definitions, source data, and metric movement are clear enough to explain line by line.
A buyer can reproduce it
Numbers are organized for diligence and exit preparation before the request list arrives.
AI can reliably analyze it
Claude and ChatGPT work from trusted metric outputs rather than inventing the underlying calculations.
The SaaS Metrics Implementation Sprint
A hands-on implementation program for finance teams that need clean data, defensible metrics, a board-ready dashboard, and an AI-ready reporting layer.
Build it on your company’s own data.
- 30-day core implementation, review week, and AI metrics capstone
- Company pass for up to two participants
- Annual access to SoftwareMetrics.ai
- API/MCP connection to Claude and ChatGPT
- Lifetime access to the SaaS Metrics Foundation course
- A repeatable process your finance team can maintain after the Sprint
Built for implementation, not passive learning.
“Ben taught me exactly how to make our finances into a repeatable process that made us look great in front of investors… overwhelming value.”
“I have since applied the learnings in three different software businesses, all with great success. I started teaching the content to my teams, which greatly helps to professionalize organizations and make people more data driven.”
Do not automate the reporting layer before you can trust the data layer.
Build SaaS metrics that management can operate from, the board can trust, a buyer can reproduce, and AI tools can reliably analyze.