Eight engagements. Every claim is specific, every outcome is verified, and every number is real. No vague "improved efficiency." No "significant cost reduction." The exact dollar amount. The exact number of days. What happened and what it cost.
Four days before the investor deadline, the CFO called us with no financial model at all. We built a fully wired 3-statement model, ARR waterfall, headcount plan, and three scenario cases. Investors never asked for a revision. The $28M round closed on time at target valuation - because on day 72, the numbers told exactly the right story.
The controller had three people working 14-day closes every month - weekends included. We mapped all 87 close tasks, automated 34 entirely, and AI-assisted 28 more. Three months later: 3-day close, $180K in annual overtime gone, same team handling double the transaction volume.
The audit committee got two quotes: Big 4 at $1.32M with a 12-month timeline. Us at $240K with an 8-week delivery. They took a chance. First-year SOX program fully implemented, zero material weaknesses at audit, IPO proceeded on schedule. The CFO later called it the best vendor decision of her career.
Six weeks. That's how long the CFO was spending each quarter producing the board package. Now it runs automatically 10 days before every board meeting - live data from Stripe, Salesforce, and NetSuite; AI-written commentary in the CFO's voice; delivered to Slack by 6am. She spends 4 hours reviewing it instead of 6 weeks building it.
A PE firm called us on a Monday. The seller had a competing bid and a hard deadline - Friday. Big 4 passed on the timeline. We ran a full QoE analysis, revenue recognition review, customer concentration risk, and management Q&A memo in parallel. Thursday evening they had a complete diligence package. The deal closed. Total cost: $38K.
Every Monday at 6am, the VP Finance used to open three browser tabs and spend the first four hours of her week rebuilding a number everyone would argue about by noon. We automated the entire pipeline - live Salesforce, Stripe, and HubSpot data, AI-written variance commentary in her voice, delivered to Slack before anyone's alarm goes off. She's reclaimed 400 hours a year. The forecast is now trusted because nobody touches it.
The finance team was spending three days every close manually reconciling what Salesforce said closed against what NetSuite said was booked. The numbers were always different. The arguments were always the same. We built a bi-directional integration that auto-reconciles bookings to revenue the moment a deal closes - zero manual re-entry, 8 days off the close cycle, and finance and sales finally looking at the same screen.
The client was a family office that had been allocating to prediction markets manually - watching screens, sizing positions by gut feel, executing late. They asked if we could build something that did it properly. We built a fully autonomous system: AI signal detection, Kelly Criterion position sizing, automated execution, circuit breakers, daily P&L reporting, and a complete audit trail. It runs 24/7 with zero human intervention. In the first 8 months of live operation: 34% returns. The client has since increased allocation.
Talk to Sophie – describe your situation and she'll scope a proposal based on what we've done before, with real numbers specific to your company.