The ad account counted shopping carts as if they were sales. One month showed $627K in reported results against $22.8K of real purchases, and the newest campaign was learning from those phantom numbers.
Client work
Three recent engagements: what we found, what we built, and what changed. Client names stay private.
The ad account counted shopping carts as if they were sales. One month showed $627K in reported results against $22.8K of real purchases, and the newest campaign was learning from those phantom numbers.
A full teardown of the ads, the product feed, and the analytics. Then a product-level margin model built from true landed costs, feeding a weekly dashboard.
Pricing and ad decisions now run on real numbers. The project grew into a monthly role steering the whole ecommerce operation.
Sales lived in one system and production costs in another, and nothing matched. No one could say which products made money.
A product-by-product profit map. All 285 production orders matched to their exact cost, twelve months of fees reconciled, every claim tied to a real order number.
The owner knows her margin on every product and which ads pay for themselves. Next build on the list: orders that flow to the printer without anyone touching them.
Five phone lines and two people answering them. After hours and during busy stretches, callers hit voicemail, and people who need a lawyer call the next number on the list.
An AI intake line that answers in English or Spanish, collects the case details, flags urgent matters, and emails the office a structured summary about 30 seconds after the call ends. It introduces itself as AI and never gives legal advice.
Test calls end with intake details in the inbox before anyone has picked up a phone. The firm is piloting it for overflow and after-hours calls.
Details shared without names on purpose. Happy to walk through any of these live on a call.