Lia is a front-desk and back-office system built to measure for multi-location operations — not a SaaS you configure, but software designed around your actual process.
Built and validated in a real operation — running across multiple locations, every day.
Let's see if Lia fitsIt's not a lack of access to AI. It's a lack of fit.
Every front-desk assistant promises to be quick to set up. The problem is that "setting up" usually means squeezing your process into someone else's — a different price list, a different intake flow, a different way of recognizing who's already a customer. In practice, that just becomes one more system disconnected from the real operation.
Lia was built the other way around: I first understood the real front-desk process of a real operation, location by location — then built the system on top of it. Every new rollout starts the same way: with diagnosis, not a template.
Five fronts, all in real production today — not a demo script, but a system that already runs the daily reality of front desk, staff, and management across a multi-location operation.
The conversation happens through a link delivered by WhatsApp Business's free automatic greeting — no paid integration with the official API, no per-message limit, no dependency on Meta approval to work.
The price list is automatically synced with the operation's management system every few hours. The exact price is only released once the right document is identified — which also keeps the price list from being copied by the curious.
The customer sends a photo of the order; the AI identifies the item, who ordered it, and the data that matters — no manual typing at the front desk, no dependency on who's on shift that day.
When Lia spots an inconsistency — a mismatched name or birth date, for example — it opens the pending item directly inside the management system the team already uses every day, with no new tool to learn. In a real operation, this cut exams that had to be redone due to data errors by more than 90%.
What the AI doesn't know becomes a pending item routed to the right person, and the answer flows back into the same conversation — the customer never has to look for another channel, and nothing goes out without a human able to review it.
Daily cash closing per location and automatic reconciliation of partner payouts run behind the scenes — the same read on the operation applied to finance, not just to service.
A multi-location service network was losing time and accuracy to manual data entry at the front desk — each location doing it its own way, a price list that was often out of date, and little visibility into what was actually breaking day to day.
Lia: an AI-assisted service system, validated in one pilot location before being rolled out to the rest — with automatic price syncing, document reading from a photo, and escalation to a human whenever needed, with an approval checkpoint before any sensitive confirmation.
Consistent process across locations, less manual entry, faster responses to customers — without removing human oversight from anything that reaches them.
Lia was built for businesses with service across multiple locations or multiple points of contact — front desk, staff, and management working off the same information, instead of each doing it their own way.
Just like in my consulting work, the path is always the same: diagnose the real process before deciding what to automate.
If any of this sounds familiar, that's the conversation I want to have.