Loyal

Healthcare's All-In-One Consumer Experience Platform

251 - 500 employees

101 - 250 engineers

$10m - $25m funding

Pre-series a

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At Loyal, we provide white label platform technology that allows health systems to make the consumer experience of getting treatment easier for patients. Think finding a doctor or scheduling an appointment. At surface level you'd think these things are easy, but each system comes with complicated backend systems and mind-numbing operational challenges. We thrive in the pre-care and post-care challenges of getting patients into and out of care seamlessly.

Why join us?

We've been around for 4 years and raised limited funding (less than 2M) and have doubled revenue every year.
Our founder and CEO has successfully exited in the space before, and our core exec team has been in the healthcare space for many years. We understand how to position our product and to execute in a way that works for the US healthcare system.
The US healthcare industry is extremely large and dominating that landscape are large health systems. A health system is a network of clinics, hospitals and ambulatory centers that treat patients. Usually there are 2 or 3 large ones in each major metropolitan area. We sell directly to these health systems (each system is easily in the 1B+ in terms of real revenue)

With healthcare interoperability right around the corner, we feel positioned to make a real impact in the healthcare space - one that empowers the patient voice - but still keeps the patient relationship with the health system. We're big believers that the privilege of the doctor-patient relationship needs to continue to exist.

Engineering at Loyal

Engineering team and processes

Our engineering team is usually split between frontend and backend. We run weekly deploys (and b/c of the regulatory nature of healthcare, there's a heavily involved documenting process).

Like most agile teams, our products are built around 2-5 engineers with designers and PMs sprinkled in. We have a Release Planning meeting every 2 weeks where we go over the current iteration and backlog and point if we can. Lots of our communication is handled asynchronously and we try to mitigate the amount of meetings we have. Only 2 stand-ups a week.

We utilize CI/CD and have mostly a services driven architecture.

Technical Challenges

Building ML into consumer facing apps like chatbot, search, surveys in a specific domain (healthcare)
Healthcare NLP being wildly different (e.g. the word shadow means one thing in healthcare, something else completely for Google)
Dealing with wildly different data pipelines in (sometimes) arcane data formats
Dealing with old EMR data structures (look up HL7 if you want to pipe delimit yourself to death)

Tech stack

React

Azure Cloud

SQL

Python

Keras

TensorFlow

Kubernetes