Eight Specialists. Zero Generic Assistants.
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.
A single do-everything bot is a worse product than eight specialists.
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.
Specialists, not generalists
Each employee is built and trained around one responsibility, so it's genuinely good at it — not a jack-of-all-trades approximation.
Real handoffs, not silos
Amy books it, Morgan collects intake, Kim follows up on the treatment plan — the same coordinated sequence a well-run front desk already follows.
One coordinator watching all of it
Trisha maintains a live view across every employee, catching what needs a human before it becomes a complaint.
One coordinator, seven specialists.
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.
Receptionist
Schedule Optimizer
Case Acceptance
Patient Intake
Follow-up
Patient Experience
Fax Management
Eight agents. Eight jobs. One coordinated platform.
Each one owns a specific responsibility — not a thin summary, the actual scenario it solves and the workflow it runs.
Amy
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.
Leena
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.
Kim
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.
Morgan
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.
Stephanie
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.
Trisha
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.
Elise
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.
Joseph
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.
This same team powers everything else on this site.
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.
What people actually ask about the team.
The whole team, one platform
Every employee here is the same platform your business runs — no separate builds, no separate bill per role.
Book a PilotSee the whole team take a real call.
Bring your actual booking rules and edge cases — no generic script, no canned demo.
Book a Pilot