Every engagement below is anonymized to protect client confidentiality. The industries, financial situations, and outcomes are real. The names aren't.
A music tech platform with real traction — 40K+ users, meaningful ARR — was preparing for a Series A. Their existing model was built in a weekend before a pitch. It had no cohort logic, no scenario modeling, and assumptions that wouldn't survive a single LP conversation.
"Investors specifically called out the quality of our projections. That was the moment I knew we had something different."
We rebuilt the entire model from first principles. Subscription economics by tier, artist revenue share modeling, cohort retention by acquisition channel, and three-scenario fundraising architecture tied to specific operational milestones.
A healthcare services company came to us believing they had nine months of runway. The founder was wrong by three months. Their model didn't account for slow insurance reimbursement cycles, seasonal staffing costs, or a collections lag that had been quietly compressing cash for two quarters.
We built a 13-week and 30-week cash flow model, identified three specific levers — payer mix optimization, overbooking strategy by insurance type, and a deferred hiring schedule — that extended runway without requiring an emergency raise.
They raised strategically eight months later. On their terms.
A consumer products company was growing — top-line revenue up 60% YoY — but margins were compressing and the founder couldn't explain why. Their blended CAC looked fine. Their blended margin looked fine. Blended numbers lie.
We broke down the economics by channel, by product, by customer cohort. What we found: new customers were generating higher AOV than repeat customers — counterintuitive, and a signal that retention economics were being obscured by acquisition mix. Amazon was profitable. Shopify direct was not, at current ad spend levels.
Reallocating 30% of DTC ad budget to Amazon and rebuilding the retention email sequence changed the margin profile within 90 days.
A real estate tech startup was in conversations with a 0M family office. The term sheet was close — but the investor wanted a model that showed 5-year projections with clear sensitivity analysis, a use-of-funds waterfall, and a realistic path to profitability that wasn't built on hockey-stick assumptions.
The previous model didn't have any of that. It was a spreadsheet with optimistic numbers and no architecture behind them.
We rebuilt it. Three scenarios, full driver-based assumptions, sensitivity tables on key variables, and a use-of-funds deployment schedule tied to specific operational triggers. The investor closed within 60 days.
A B2B SaaS founder was 60 days from a Series A. Every time an investor asked a what-if question during diligence, the answer was the same: I will get back to you. That lag was costing credibility and momentum.
We built an AI-powered scenario engine on top of their financial model. Natural language inputs. Instant outputs across pricing, churn, headcount, and growth assumptions. The founder could answer any investor question in the room, in real time, with a number behind it.
The investor told them they had never seen a founder that prepared. They cited the financial clarity specifically when they sent the term sheet.
AT3 rebuilt our entire financial model before our Series A. Investors specifically called out the quality of our projections. We closed in 8 weeks.
I thought I had nine months. Tyson showed me I had six. That conversation saved the company. We rebuilt around the real numbers and raised without panic.
Nobody had ever broken down our unit economics by channel before. The Amazon vs. Shopify analysis alone was worth the entire engagement. We stopped bleeding margin within 90 days.
Every case study above started with one conversation. Let's find out what yours leads to.