A US healthcare analytics platform aggregating data from ~400 aesthetic surgery offices. Stalled ingestion, dated UI, falling adoption. Production-ready in six weeks.
Weeks To Production
Surgeon Offices Aggregated
Months To Resumed Growth Motion
Data the surgeons could finally rely on
The client aggregates clinical data from roughly 400 aesthetic plastic surgery offices across the US and surfaces it back to each surgeon as a benchmarking dashboard. Aesthetic surgery sits outside insurance — peer benchmarks on case volume, pricing, and mix materially influence how each practice is run. The dashboard is the product.
- Unreliable ingestion from a proprietary in-office device — sync failures and duplicate records polluting the dataset.
- Dashboard UI looked dated; daily usage falling among existing surgeons.
- Sales motion stalled — could not credibly sign new offices into the network.
- Rewrote the ingestion layer — fixed sync and dedup so the dataset could be trusted again.
- Redesigned the dashboard into a clean, minimalist interface.
- Kept the stack pragmatic — AngularJS on the front, .NET on the back. No rewrite the timeline couldn't absorb.
THE OUTCOME
Once the data errors disappeared, daily usage climbed among existing surgeons. By month three, the client was signing new surgeons into the network — a growth motion that had been stalled before we came in.
Why this turnaround was possible
Mobifilia is a 14-year product engineering firm based in India, working with clients in the US, UK, Canada, and Europe. We take every client we engage with to market — over 14 years, that is 100%.
THE NATURAL - CHOICE EQUATION
In business — running live products since 2012.
Every client we have engaged has reached market.
Value Adders and Software Builders own outcomes.
We work where we know the domain — and say so when we don't.
We use AI in our own delivery pipeline and marketing engine.
Information security under an audited framework.
WHEN TO CALL US
Distributed data ingestion at scale. Member- or customer-facing dashboards that have stopped earning trust. A legacy stack that works but needs to be brought back to current. Tight timelines that don't have room for a multi-quarter rebuild.
