Algoricum
The follow-up that keeps going until they book.
An AI system that remembers each patient conversation and picks it back up at the right moment, in the clinic's own voice.
- Client
- Algoricum
- Industry
- Healthcare SaaS / Patient Acquisition
- Region
- United States
- Engagement
- B2B SaaS · Conversational AI · Lead Follow-Up Automation
Most clinic leads are never really lost. They just stop replying. A patient asks about a treatment, the front desk answers once, staff get busy, and the ad money behind that lead is wasted. Algoricum was built around that gap. Hashlogics helped build the AI system that tracks each conversation and follows up at the right moment until the inquiry becomes a booking.
The problem we set out to solve.
Clinic staff couldn't follow up with every inquiry, every time.
Nights, weekends, and busy periods created long response gaps.
CRM sequences fired on timers, not on the context of what a patient actually said.
Following up too aggressively felt robotic, while giving up too early lost viable patients.
What success needed to look like
- Respond when clinic staff are unavailable.
- Remember the full conversation, even after several days of silence.
- Match each clinic's services, pricing, policies, and tone.
- Know when to book, when to keep nurturing, and when a person should take over.
- Work beside the clinic's current systems instead of replacing them.
How we delivered it.
- 01
Diagnose
Mapped where patient chats went quiet and why timer-based CRM tools failed to bring them back.
- 02
Design
Modeled follow-up around conversational state instead of a fixed day-one, day-three, day-seven sequence.
- 03
Build
Created the AI chat layer, clinic knowledge setup, lead state, follow-up rules, the dashboard, and booking flows.
- 04
Launch
Cut setup friction so a clinic can load services, pricing, FAQs, tone, and rules fast.
- 05
Run
Used real conversations to sharpen timing, replies, escalation, and booking behavior.
What we built.
Algoricum behaves like a tireless front-desk teammate rather than a drip campaign. If someone asks about a treatment and says they need to check their schedule, the system remembers that context. When the thread goes quiet for two days, it resumes that specific conversation instead of restarting from a template. It answers after hours, handles common questions, moves interested patients toward an available appointment, and hands off to a person when judgment is needed.
Results & impact
+19%
inquiry-to-booking conversion, first month at one clinic
Algoricum-published client results, 2026
3.8
average messages per booking
Algoricum-published client results, 2026
Whose numbers these are.
Algoricum reports these results from Glow Aesthetics, a clinic on the platform, along with bookings recovered from silent inquiries in the first month. The figures are published by Algoricum and its client, not measured by us.
The AI itself isn't the hard part. The hard part is knowing what happened three days ago, why the patient went quiet, and how to pick the thread back up without sounding canned. That memory is what turns the product into an engine for missed bookings.
