For clinics we automate the back office — patient intake, referrals, reminders and admin paperwork — and never clinical decisions. Access is role-based, data stays inside your infrastructure where it has to, and anything a patient sees keeps a human sign-off. On our clinic network project no-shows fell from 20% to 8% and 50–60% of contacts closed without staff.
Forms, IDs, and insurance details are typed into the EHR by hand, delaying care and inviting errors.
Inbound referrals and faxes queue for staff to read, classify, and route to the right department.
No-shows and rescheduling eat front-desk time that should go to patients in the room.
Information lives in the EHR, PDFs, and portals that don’t talk to each other.
Read intake forms, IDs, and insurance cards and populate the EHR with human verification.
Classify inbound referrals and faxes, extract key data, and route to the right department.
Automate booking confirmations, reminders, and waitlist fills to cut no-shows.
Keep records consistent across EHR, portals, and billing over secure, monitored connections.
of contacts closed by AI
Client under NDA. The figures are the client’s own, comparing the periods before and after launch.
This is the project behind the case above, described the way it happened. A network of five clinics, over a thousand staff. Bookings arrived by phone, from the website and from aggregators and landed in different places; reminders went out whenever someone at the front desk had a spare minute. A fifth of patients did not turn up, and the slot they freed stayed empty because nobody found out in time. We rolled it out branch by branch — each loop ran in one clinic before it went to the other four.
Phone, website, aggregators and the front desk all write into the same schedule. Double bookings and the manual re-entry between systems went with it.
Confirmations and reminders go out on their own, and a cancelled slot is offered to the waiting list straight away. No-shows fell from 20% to 8% and chair utilisation rose by 10–15%.
Booking, rescheduling, prices, how to prepare for an appointment — the routine half of the calls is handled automatically, around the clock. 50–60% of contacts close without an operator.
The platform knows what a patient is due next — a repeat appointment, a control test, a routine screening — and the reminder goes out on time instead of waiting for the next visit.
Test results reach the patient directly once they are released, and the queue at reception goes with them.
Complaints are sorted by type and the urgent categories skip the queue. Calls are scored automatically, so a supervisor listens to what actually needs it rather than to a random sample.
Three months to the full loop across the network. Most of the effort went not into the automation but into the schedule: five branches had five different ways of building one. Clinical decisions were never in scope. What deliberately stayed with people: everything medical, complaints about the treatment itself, and the 40–50% of contacts the AI hands to an operator.
One to two weeks. Fixed, and credited in full against the build.
A fixed quote against the scope the audit produced.
On this project. Two to four months is the range across our cases.
Model and infrastructure usage plus maintenance. Moves with volume.
We build scheduling, intake, coding support, claims and correspondence. We do not build anything that contributes to a diagnosis or a treatment decision, and we decline that scope when it is offered.
Patient data is minimised at ingestion, processed in-region and logged on every access. Where a flow does not need an identifier, it never receives one.
Anything that reaches a patient or a payer is reviewed by an accountable person before it goes out. The automation prepares; it does not send.
Retention differs by jurisdiction and by record type, so it is configured and enforced in the system rather than described in a policy document.
Reading incoming referrals, extracting what the system needs and flagging what is missing before it reaches a coordinator.
Reminders, rebooking and waitlist filling — a hard number that moves without touching clinical content.
Checking claims against payer rules before submission, and assembling the appeal packet when one comes back denied.
Diagnosis, acuity triage, dosing and anything else that is clinical decision support: out of scope, deliberately.
If your systems have no API and no interface engine, budget for that before budgeting for automation.
Handwritten clinical notes are still unreliable to extract, and we would not build a process that depends on it.
Each page states the workflow, the systems it integrates with, what it costs and when it is the wrong choice.
Collects, checks and files everything a new client has to hand over, and chases whatever is missing.
Sorts mixed incoming documents by type and routes each one to the queue, folder or system that owns it.
Turns sales and support calls into structured notes, objections, next steps and CRM updates.
Yes. We design to HIPAA and GDPR — data minimisation, encryption, access controls, BAAs/DPAs, and audit logging. PHI stays inside your approved environment.
We don’t propose an off-the-shelf product either. The audit is there precisely to see where your process differs from the ordinary one, and the build is shaped around that difference. And if it turns out a standard tool covers you well enough, we’ll say so plainly — the process map stays with you in any case.
It’s a fair thing to ask, and responsibility matters more here than accuracy. A person answers for it, and the system is built so that they can: a doubtful case is never posted quietly, it goes to a review queue. Low confidence isn’t an error for us, it’s a route. Around 70% clears straight through and a person looks at the rest — and where exactly that line sits is yours to decide.
That’s a common requirement, and a reasonable one. We fit the setup to your data rules: if nothing may leave, the model runs locally inside your own perimeter, and the documents never cross it.
That’s not unusual, and it’s fine. Integrating without an API is the most underestimated line in a quote, which is why integration surface comes first among the four cost drivers, and why we don’t name a fixed price before the audit. Working without an API is perfectly possible — files, exports, email — it simply costs more, and it’s better to know that early. Which is why, fairly often, we build the API for you.
It happens often, and usually for one reason: the pilot was measured on the happy path and the exceptions were left for later — though the exceptions are where most of the work turns out to be. So we look at the share that clears without a person rather than at extraction accuracy, and we agree in advance who handles the rest, and how.
No, that isn’t what this is about. What goes is the retyping, not the people: decisions stay with a person — the disputed document, the non-standard transaction, the conversation with the client, the signature under the reporting. On one accounting project, closing a client month went from three days to four hours, not because anyone was let go, but because a qualified specialist stopped keying in details by hand. The time that frees up most often goes into growth: more clients with the same team, without the costs rising alongside.
It’s a fair question, and sometimes doing it yourselves really is the right answer. We say so when the volume doesn’t justify a build: below roughly 300 documents a month the arithmetic usually doesn’t work. The calculator on this site includes running costs, so you can weigh that up before you ever talk to us.
They do change, and that’s exactly what we build for: the model is a replaceable part here, not the foundation. The source of truth is our own database — the document, the counterparty, the entry. The model is attached to the side, and we update it whenever something better appears; for you that’s planned maintenance, not a rebuild.
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