SaaS operators, growth leaders, and finance teams who need to track subscription metrics, understand user engagement, and monitor business health from their production database.
The problem
What saas teams deal with every day.
Subscription metrics are hard to calculate
MRR, churn, expansion revenue, and net retention all require complex calculations across billing, subscription, and usage data. Most teams get it wrong or give up.
Usage and billing data don't connect
Your product usage data lives in one database and billing in another. Understanding which features drive upgrades or which usage patterns predict churn requires manual analysis.
Growth experiments lack measurement
You launch a new pricing tier, adjust onboarding, or test a feature gate, but measuring the actual impact on revenue and retention takes weeks.
Investor and board reporting is manual
Every month, someone spends a full day calculating SaaS metrics, building charts, and compiling a board deck. The process is error-prone and time-consuming.
How AI for Database helps
Ask questions, get answers, automate everything.
Automated SaaS metrics
Get accurate MRR, ARR, churn, expansion, and net retention calculated from your actual subscription data. No spreadsheets needed.
> Show me MRR, MRR growth rate, gross churn, and net revenue retention for each of the last 12 months
Usage-to-revenue correlation
Connect product usage patterns to revenue outcomes. See which features drive upgrades and which usage drops predict churn.
> Which product features are most used by customers who upgraded in the last 90 days vs. those who churned?
Experiment impact analysis
Measure the revenue and engagement impact of pricing changes, onboarding updates, or feature launches with simple queries.
> Compare trial-to-paid conversion rate and average contract value for the new onboarding flow vs. the old one
Investor-ready reporting
Generate board-ready metrics dashboards that update automatically. Share live links instead of static slides.
> Create a monthly investor dashboard with MRR, net retention, CAC payback, LTV/CAC ratio, and logo churn
Cohort and segment analysis
Analyze retention, expansion, and engagement by signup cohort, pricing plan, company size, or any other dimension.
> Show me 12-month revenue retention curves by pricing plan, with cohorts grouped by signup quarter
Dashboard templates
Automated workflows
Key metrics you can track
“We used to argue about whether our churn was 3% or 5% because everyone calculated it differently. AI for Database gives us one consistent answer from real subscription data.”
James W.
VP of Finance, B2B SaaS Platform
Frequently asked questions
How does AI for Database calculate SaaS subscription metrics like MRR and churn?
AI for Database connects directly to your billing and subscription database and calculates MRR, ARR, gross churn, net revenue retention, expansion revenue, and contraction from your actual transaction records. The platform handles the complexity of proration, mid-cycle upgrades, downgrades, and cancellations that make these metrics notoriously difficult to calculate in spreadsheets. You ask a question like "show me MRR and net retention for each of the last 12 months" and get an accurate, consistent answer. This eliminates the common problem where different teams calculate churn differently and argue over whose number is correct.
Can AI for Database connect product usage data to revenue outcomes?
Yes. AI for Database excels at correlating usage patterns with revenue metrics because it can query across your product database and billing system in a single question. You can ask which features are most used by customers who upgraded recently versus those who churned, or which usage patterns in the first 30 days predict long-term retention. This usage-to-revenue connection is critical for SaaS businesses but typically requires a data engineer to build custom pipelines. AI for Database makes it accessible through plain-English questions, helping product and growth teams prioritize features that actually drive revenue and reduce churn.
How does AI for Database help measure the impact of SaaS growth experiments?
AI for Database lets SaaS teams measure experiment impact by querying before-and-after metrics with simple questions. When you launch a new pricing tier, adjust the onboarding flow, or test a feature gate, you can ask "compare trial-to-paid conversion rate and average contract value for the new onboarding flow versus the old one" and get an instant comparison from real data. The platform removes the weeks-long delay that typically exists between launching an experiment and getting conclusive results. Growth teams using AI for Database can iterate faster because the feedback loop from change to measured outcome shrinks from weeks to minutes.
Can AI for Database automate investor and board reporting for SaaS companies?
AI for Database automates the monthly investor reporting process that typically consumes a full day of a finance or operations leader's time. You build a dashboard once with the metrics investors care about, including MRR waterfall, net retention, CAC payback, LTV-to-CAC ratio, and logo churn, and it updates automatically from your live data. Share a link with board members so they always see current numbers, or schedule a monthly email with a formatted metrics snapshot. AI for Database ensures every metric is calculated consistently from the source of truth, eliminating the manual errors and version-control headaches that plague spreadsheet-based board reporting.
Ready to try AI for Database?
Query your database in plain English. No SQL required. Start free today.