A receptionist. A scheduler. Someone chasing unpaid treatment plans. Real front offices split the work between specialists — so we built eight AI Employees that do the same, each owning one job and handing off to the next exactly like a real team would. This is the whole product. Everything else on this site is this team, deployed somewhere specific.
Illustrative — not a real-time call log
A receptionist, a scheduler, and someone recovering unpaid treatment plans are different jobs. Collapsing them into one generic assistant doesn't make it more efficient — it makes it worse at all three, the same way one employee trying to do every front-office job at once would be.
Each employee is built and trained around one responsibility, so it's genuinely good at it — not a jack-of-all-trades approximation.
Amy books it, Morgan collects intake, Kim follows up on the treatment plan — the same coordinated sequence a well-run front desk already follows.
Trisha maintains a live view across every employee, catching what needs a human before it becomes a complaint.
Trisha maintains a real-time view across every other employee — what's stalled, what's working, and what needs a human — so the team operates as one system, not seven disconnected tools.
Each one owns a specific responsibility — not a thin summary, the actual scenario it solves and the workflow it runs.
It's 6:45pm. Your front desk left at six. A new patient calls about a toothache that's been bothering them since lunch. Amy answers on the second ring, understands why they're calling, checks real availability, and books tomorrow's first opening — before they've even considered calling the practice down the street.
Full workflow & pain points →A patient cancels their 2pm at 11am. Without someone actively working the waitlist right now, that slot sits empty for the rest of the day. Leena identifies the cancellation the moment it happens, matches it against the waitlist by urgency and fit, and has the slot rebooked before lunch.
Full workflow & pain points →A patient leaves a consult with a treatment plan and a "we'll call you to schedule" — and then nobody does, because the front desk moved on to the next patient in the chair. Kim follows up within a day, answers the cost question that was actually holding them back, and gets the case back on the schedule instead of quietly written off.
Full workflow & pain points →A patient arrives for their first visit and spends ten minutes filling out a clipboard of paperwork in the waiting room — pushing the whole day's schedule back before it even starts. Morgan already sent the intake packet after booking, collected history, allergies, and insurance digitally, and synced it to the PMS or EHR — so the patient walks straight back.
Full workflow & pain points →A patient's due for their six-month recall. Nobody calls, because the recall list has three hundred names on it and staff are working the phones that ring, not the ones that should. Stephanie works the entire recall list continuously, reaching out at the right interval for each patient and getting them rebooked before they've gone long enough to become a lost patient entirely.
Full workflow & pain points →A scheduling conflict and an unusually tense caller happen within the same ten minutes — exactly the kind of moment that gets missed when everyone's heads-down on their own task. Trisha sees both the moment they happen, recognizes the caller needs a human right now, and routes it — while the scheduling conflict gets resolved automatically in the background.
Full workflow & pain points →A patient has a genuinely great visit and nobody asks for a review — meanwhile, a single frustrated review from months ago is still sitting unanswered, publicly, for every prospective patient to see. Elise catches both: prompting the happy patient at the right moment, and drafting a thoughtful, timely response to the older review the same week it's flagged.
Full workflow & pain points →A referral fax arrives overnight. By the time someone finds it in the machine and reads it the next afternoon, the patient has already called somewhere else. Joseph reads it the moment it arrives, extracts the relevant information, and routes it into the right workflow — the referral gets contacted the same morning, not a day and a half later.
Full workflow & pain points →Dental Automated and Medical Automated deploy the full eight-employee team. AISC Booking Agent is Amy alone. Other industries deploy the employees that fit their volume.
Amy alone, standalone for booking.
The full 8-agent team, dental front office.
The full 8-agent team, medical front office.
Selected agents, scoped to service scheduling.
Selected agents, scoped to reservations.
Selected agents, scoped to reservations & waitlists.
Selected agents, scoped to ordering & pickup.
Because a receptionist, a scheduler, and someone chasing unpaid treatment plans are three different jobs with three different skill sets — combining them into one generic assistant makes it worse at all three. Specialization is why this works; one coordinated team outperforms one do-everything bot the same way a real front office does.
No. Dental Automated and Medical Automated deploy the full team, but AISC Booking Agent is Amy alone, and other industries can start with just one or two agents and add more as needed.
No. It takes on the volume of calls, scheduling, and follow-up work that's difficult for any front desk to keep up with consistently — Trisha is built specifically to recognize what still needs a human and hand it off.
Yes — each one works continuously on its responsibility, coordinated by Trisha, who maintains a real-time view across all eight.
Same eight agents, same underlying platform — the role labels and specific workflows adapt to the vertical. Stephanie runs hygiene recall in a dental practice and general follow-up care in a medical one; the agent underneath is the same.
Bring your actual booking rules and edge cases — no generic script, no canned demo.
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