I’ve been testing an agent that preps local business projects before sending them into Lovable. Curious if this is overkill or actually useful.
I’ve been building a bunch of local business site demos in Lovable, and I kept running into the same problem.
Lovable is fast, but the first result depends a lot on what you feed it.
For local businesses, the info is usually all over the place.
Sometimes there’s no website. Sometimes there’s an old one that barely matches the actual business. Sometimes the only useful info is Google Maps, a few reviews, some photos, and whatever you can figure out from the menu/services.
So I started testing an agent that runs before Lovable.
It doesn’t build the site directly.
It takes the messy business info and turns it into something Lovable can actually use:
- quick business brief
- what the current site/presence is missing
- page structure
- section plan
- copy direction
- CTA ideas
- missing info/questions for the owner
- then a full Lovable build prompt
The more intersting part is after the first build.
I’ve been using the same agent almost like a site architect after Lovable generates v1.
Stuff like:
- improve the hero
- make the service/menu cards clearer
- fix weak copy
- add trust sections
- clean up mobile spacing
- replace placeholder info
- prep the site for launch or handoff
I’ve tested it across a few local business types: lounge, shawarma spot, barber, HVAC, beauty/ecomm, storage, restaurants.
The main thing I’m noticing is that the bottleneck may not be Lovable’s build ability.
It might be the pre-build layer.
Getting the business, offer, pages, and missing details clear before asking Lovable to generate anything seems to make the first output way stronger.
What’s the approach other people here use to handle this.
Do you usually prompt Lovable directly and iterate?
Write a spec first?
Use another AI to prep the context?
Or just build visually untill it feels right?
Also where do you think the line is between useful planning and over-engineering the prompt.