Hugo Maciel Fraga

I analyzed labor, utility, and overhead costs, rebuilt a years-old pricing model and the calculator reps quoted with, and built the monthly sales dashboard and KPI reporting.

Problem

Every quote started from a years-old pricing formula that had not been rebuilt from actual cost data. Separately, the web lead pipeline carried spam and unqualified traffic.

What I did

• Rebuilt the pricing model from actual labor, utility, and overhead data to reflect fabrication cost and margin.
• Built the interactive pricing calculator reps used to evaluate quote pricing.
• Measured waste rates on house-color slabs to derive true cost and profit per square foot.
• Built the monthly sales dashboard and KPI reporting from Pipedrive CRM and accounting data.
• Worked with marketing to filter spam and unqualified traffic out of the web lead pipeline.

Result

Close rate rose from the mid-60s in late 2024 to the high 70s in early 2026, from cleaner leads, better client discovery, and proposals tailored with the pricing calculator, working together.

close rate, from leads, discovery, and proposals combined

mid-60s to high 70s

Excel, Tableau, PowerPoint, Pipedrive CRM, accounting data.

Read the case study→: Pricing and CRM reporting

A PostgreSQL data model and inventory logic that track slab-level stock, pricing, and availability for stone fabrication shops. Sold as SaaS to paying customers.

Problem

A fabrication shop needs to know which remnant slabs it has, what each one is priced at, and whether it is still available. Grew from the remnant-tracking problem I first solved at Quick; Quick later became a paying customer.

What I did

• Designed the PostgreSQL data model and the inventory tracking logic behind slab-level stock, pricing, and availability reporting.
• Set the three-tier pricing model from cost and usage analysis.
• Built Stripe billing.
• Ran a 60-shop field campaign across Maryland and Virginia.

Result

Live, with paying customers.

shops visited across Maryland and Virginia

60

Next.js, PostgreSQL, Vercel, Stripe.

A tiered commission plan designed from sales and profit data by rep, with every tier checked against real past months.

Problem

The sales team, two reps besides me, needed a tiered commission plan grounded in what each rep actually sold and earned for the business.

What I did

• Analyzed sales and profit data by rep.
• Designed a tiered structure from that data.
• Validated every tier against real past months.
• Documented it in English and Portuguese.

Result

A tiered plan tested against real past months, documented in both languages the team works in.

Sales and profit data by rep.