A health system with 100+ clinics was stuck on a scheduling module that couldn't keep up. We built a new scheduling engine on top of their EMR instead of replacing it.

Impact, measured in results
Booked through the platform annually
Down from several minutes per appointment
Across 100+ clinics including peak volumes
A federally qualified community health system with roughly 100 clinics in the Northeast, serving underserved and underinsured populations. They schedule about 250,000 appointments a year through several hundred call center and front desk staff, reaching somewhere between 800,000 and a million patients.
What used to take hours of call center time now takes minutes. And we didn't have to leave our EMR to get there.
Operations Director
Multi-Clinic Health System
They didn't need a new EMR. They needed a scheduling engine that sat on top of the one they had. The EMR stays the system of record. All day-to-day booking moves into a UI designed for call center speed.
We wired it together with HL7 for bi-directional sync on appointments and patient data, with error handling and reconciliation built in so the two systems stay in lockstep. The rules engine lets admins configure scheduling policies by clinic, provider, visit type, and insurance program. No code changes needed.
Rollout was incremental. We started with a handful of clinics, ran both systems in parallel until staff were confident, then scaled to 100+. Each iteration was shaped by the people actually using it.
Related solutions & industry
Provided services
Our team
Justin Tannenbaum
Solutions Architect
Lukasz Chmielowski
Lead Engineer
Piotrek Szyperski
Engineering
Ernesto Quispe
Product Design
Tech stack

Stuck on a legacy system? We've built the engagement layer on top before. Let's talk about yours.
Schedule a discovery callReal-world examples of solving complex problems and scaling challenges.

A DME startup was writing off revenue because nobody could predict which placements would get reimbursed. We built an AI engine that checks every case against Medicare and Medicaid policy before anything goes out the door.
View case study
The nation's largest flat fee brokerage had a sell-side business but no way for buyers to search, discover, or transact. We built the marketplace and a proof-of-concept for AI-powered property search that understands context, not just filters.
View case study
A PE-backed study abroad provider had acquired multiple companies, each with its own HR systems, policies, and tribal knowledge. We consolidated everything into SharePoint and built a generative AI assistant that answers people ops questions across the entire enterprise.
View case study