Garza International's estimating team was spending 6+ hours per bid response. The math was simple: they could only bid on a fraction of available projects, and the ones they did bid on were priced based on stale data.
The Cost of Manual Quoting
Before the AI system, each construction quote required an estimator to:
- Pull current material costs from 4 different supplier spreadsheets
- Cross-reference labor rates against an internal rate book
- Search email archives for historical bid data on similar projects
- Manually calculate profit margins and adjust for risk
- Write and format the proposal in Word
Total time per bid: 6–8 hours. Total bids per week: at most 3–4. The team was working at capacity and still losing work to competitors who responded faster.
The Build: 3 Weeks, Fixed Price
We started with a $300 Discovery Session. One hour to identify the bottleneck, map the current cost, and outline the build. The math was clear: a 60% reduction in quoting time would let Garza bid on 2x the projects without hiring more estimators.
The system we built connects supplier pricing APIs, historical bid data, and labor rate tables into a single interface. An estimator enters project specs — square footage, material type, location, timeline — and the engine returns a fully formatted proposal in under 30 minutes.
ROI by the Numbers
Before: 6–8 hours per bid, 3–4 bids per week, estimators overloaded, inconsistent pricing across bids.
After: 30–45 minutes per bid, 10+ bids per week, consistent pricing models, automatic margin optimization based on historical win/loss data.
Investment: $300 discovery + fixed-price build. Recovered in a single large bid win.
"SanLuis AI didn't just build software — they solved the single biggest problem in our sales process. We're bidding faster and winning more." — Director of Operations, Garza International
Why Fixed-Price AI Works Here
Garza's quoting problem was well-defined. The data existed. The rules were clear. The output was predictable. That's the sweet spot for fixed-price AI builds — where the problem is clear enough that a Discovery Session can scope it completely, and the solution doesn't need months of iteration.
Not every AI project fits this model. But most workflow automation problems do. If you can explain the problem in one sentence and the data is already structured, a fixed-price build is probably the right answer.
