I use Lovable every. Single. Day. Weekends included. 😂

So when my favorite AI builder teams up with Contra for a competition, there’s no “should I enter?” Of course I’m in.

The challenge: take a small business from “can I book?” to “you’re confirmed” without the owner spending half their day chasing messages.

This one hits close to home. I’ve already built a booking flow for my own brand, pmcosta.dev, and reused it across multiple side projects. Because booking an appointment shouldn’t require twelve messages and a calendar negotiation.

Now I get to take that experience and push it further. Not just a nice-looking widget, but something that actually takes work off a business owner’s plate.

Daily habit meets competition. Let’s build. 🔥

@Lovable @Contra HQ #lovablechallenge

Title: From Sales Metrics to Cash Flow: Financial Model for a Volatile-Revenue Business

Description: Built an integrated cash flow and P&L model driven by commercial metrics — sales volume, average ticket size, and customer base growth — rather than pre-aggregated financial figures, for a business with uneven or seasonal revenue.

Challenge: The company could see sales and customer numbers, but had no clear view of how much cash that would actually generate, or when — leaving liquidity planning largely reactive.

Approach: Commercial drivers flow through three linked layers — Commercial Metrics → P&L → Cash Flow — entirely on live formulas, so the model recalculates end-to-end when any input changes. Cash conversion is modeled with a collection-lag assumption (a share of revenue collected the same month, the rest the following month), which is what actually creates the liquidity gap between "profitable" and "cash-positive."

What it surfaced (illustrative figures): In the sample run, EBITDA turned positive by month 3, but the cash balance still dipped to a low point before recovering two months later — the gap between accounting profit and available cash that the business needed to plan around. Every driver (orders per customer, ticket size, collection speed) is a single editable input, so the same model reruns instantly for a different scenario.

Outcome: Turned a sales question into a cash planning answer, with the exact month and depth of the liquidity gap visible in advance instead of discovered after the fact.