The pipeline report
that writes itself.
A Series B SaaS company was spending 20 hours a week building a pipeline report that was outdated before leadership read it. Here is exactly what I built, how it works, and what changed.
What breaks and
what it costs.
What lands in Slack
every Monday at 7am.
Representative sample of the actual report output. All data is live from HubSpot, Stripe, and GA4 - no manual input.
Pipeline Coverage: 2.4x - below the 3x target. Action required. Highlights: - Total pipeline: 4.2M (up 8% from last week) - Deals in negotiation: 12 (1.8M) - 3 stalled over 14 days - Closed-won this week: 340K across 4 deals - At-risk: Acme Corp (280K) - no activity in 18 days AI Recommendation: Prioritize outreach to Acme Corp and DataFlow Inc. Both have gone dark after demo stage. Combined value: 510K. If both slip to next quarter, you will miss coverage target by 800K. Rep Performance: - Sarah Chen: 3 deals closed, 180K - on pace - Marcus Webb: 0 closes, 8 deals stalled - needs coaching - Jordan Park: 1 close, 160K - strong week
How 14 seconds of
automation works.
Everything is built on open APIs and documented in plain language. If you want to move from Make.com to n8n, or swap Claude for GPT-4, I can do that in an afternoon. You own the automation.
What separates this from
a 150K platform.
Built in 4 weeks, not 4 months
Enterprise BI platforms take 8-16 weeks to implement. This was live in 4 weeks from kickoff. The client saw their first automated report before the enterprise sales team finished their discovery calls.
Uses your existing systems
No platform replacement. No data migration. No retraining your team on a new tool. The automation sits on top of HubSpot, Stripe, and Google Sheets - the tools you already pay for.
Fully documented and maintainable
Every scenario is documented in plain language. Your RevOps manager can read the documentation and understand exactly what is happening. No black boxes.
AI that says something specific
The AI does not just summarize the numbers. It names the deals that are at risk, explains why, and recommends specific actions. The VP of Sales stopped asking what should I do about this.
Historical trending built in
Every Monday report is saved as a snapshot in Google Sheets. After 8 weeks, you have a trend line. After 6 months, you have a pattern. No additional setup required.
Error handling that does not wake you up
If HubSpot API is slow, the scenario retries automatically. If Stripe returns an error, the report notes it and continues. Errors go to a dedicated Slack channel, not to your inbox at 7am.
What you are actually comparing.
At 78/hr fully-loaded labor cost and 4-5 hours/week recovered, this engagement pays for itself in 3-6 months. After that, it is pure recovered capacity.
Your version of this
takes 4-6 weeks.
The Stack Review is free. 45 minutes. I map your current reporting workflow, identify the top 3 automation opportunities, and give you a rough ROI estimate. No pitch. Just the math.