My Laser Doctor

Service manual · the platform

Everything the platform does, one step at a time.

The homepage shows the conversation. This page shows the machinery behind it — the problem it removes, the fifteen steps it runs, and the numbers it puts on your screen. Nothing here is abstracted into vague benefit language.

The problem

Managing 500 lasers across 200 locations by hand creates chaos, hidden costs, and downtime you can't get back.

Manual asset tracking. Managing inventory via spreadsheets across 200 locations leads to inconsistent records, lost warranty data, and "ghost" assets.

High emergency costs. A reactive repair model forces premium payments for emergency technician visits rather than lower-cost preventive maintenance.

Limited fleet visibility. No centralized insight into device utilization, specific error codes, or recurring failure patterns across the network.

Downtime and disruption. Unplanned equipment failure directly impacts revenue, causes patient rescheduling, and creates idle time for clinicians.

Fragmented vendor communication. Office managers waste time triangulating between multiple manufacturers and third-party service organizations for dispatch.

No lifecycle analytics. Without data-driven insight, "repair vs. replace" decisions are guesswork — and capital gets spent inefficiently.

Inefficiency scales linearly with practice growth. This system removes it.

How it works

The process, step by step

Every step below happens on a single text thread. Numbering is the actual order the platform runs in.

  1. PHASE 1 · IDENTIFICATION
  2. 1

    Staff sends photo

    Human

    Staff texts a photo of the device label to a location-specific number.

    Replaces: looking up model/serial in a binder or spreadsheet.

  3. 2

    AI decodes image

    AI

    Computer vision extracts manufacturer, model, and serial from the photo.

    Replaces: typing asset details into a tracking sheet by hand.

  4. 3

    Database lookup

    AI

    The platform matches the extracted ID against the fleet database to resolve exact specs, warranty, and service history.

    Replaces: an office manager digging through paper warranty cards and old service invoices.

  5. 4

    SMS confirmation

    AI

    "I know this machine. What's wrong?" — texted back to staff.

    Replaces: a phone call back-and-forth just to confirm which machine is broken.

  6. PHASE 2 · DIAGNOSTICS
  7. 5

    Staff describes issue

    Human

    Staff describes the problem in plain language — no error-code lookup required.

    Replaces: staff guessing at technical terms to explain the problem to a vendor rep.

  8. 6

    AI diagnostic Q&A

    AI

    Iterative questions isolate the cause, drawing on the manufacturer's own error-code knowledge base.

    Replaces: an office manager on hold, reading error codes off a screen to a call-center script.

  9. 7

    Troubleshooting

    AI

    AI walks staff through guided safe-fix steps — restart, calibrate, clean lens — in plain text.

    Replaces: a technician dispatched for problems staff could have fixed themselves.

  10. PHASE 3 · DISPATCH
  11. 8

    Request technician

    AI

    If the guided fix doesn't resolve it, the platform flags that a technician is needed, and why.

    Replaces: staff deciding, without data, whether the problem is serious enough to call someone.

  12. 9

    Confirm request

    AI

    The AI asks staff to confirm before anything is booked.

    Replaces: nothing today — this approval step doesn't currently exist.

  13. 10

    Staff confirms

    Human

    A one-word yes, and nothing further is required of them.

    Replaces: staff staying on the phone through an entire booking call.

  14. 11

    AI calls the manufacturer

    AI

    A voice call to the OEM or ISO, navigating their phone tree, to book a technician.

    Replaces: an office manager holding on the phone with a manufacturer's service line.

  15. 12

    Check calendar

    AI

    Verifies the clinic's calendar availability before proposing a time.

    Replaces: back-and-forth emails or calls to find a time that actually works.

  16. 13

    Book technician

    AI

    The appointment is negotiated and booked directly with the vendor's dispatch desk.

    Replaces: manual scheduling and confirmation calls.

  17. 14

    Add to calendar

    AI

    The visit is added to the clinic's calendar automatically.

    Replaces: someone remembering to write it down.

  18. PHASE 4 · ANALYTICS
  19. 15

    SMS confirmation, logged

    AI

    Staff get the appointment details, and the technician arrives already briefed on the error code, symptoms, and parts to bring. Every step of the exchange is logged for the fleet analytics behind the KPI dashboard below.

    Replaces: a technician showing up cold, re-diagnosing a problem that was already solved over text.

Humans stay in the loop. Staff report the issue in their own words (step 5) and approve every technician dispatch before it's booked (step 10) — nothing about a warranty claim or a safety-critical repair is ever decided by the AI alone.

The platform

What we handle for you

AI Photo ID

Instant photo-based recognition of device make and model. Eliminates manual entry errors and speeds up intake to seconds.

Demo: 500 assets, zero manual entry.

Diagnostic Knowledge Base

A comprehensive library of error codes and symptoms drives the AI troubleshooting engine for instant self-resolution steps. Certified technicians upload manufacturer manuals once — every clinic's diagnostics improve the same day.

Demo: manufacturer error codes across the full fleet, cited to source.

Automated Dispatch

Routes unresolved issues directly to OEM or ISO partners with rich data payloads — logs, photos, location — skipping the phone queue.

Demo: dispatch booked and confirmed without a single phone call.

Fleet Analytics

Real-time ROI and health data across the network — unified tracking across 200+ sites in a single pane of glass, plus preventive-maintenance scheduling tracked to completion.

Demo: 200 locations, eighteen months of downtime and cost history, zero empty charts.

Live demo data

The numbers a fleet operator watches every day

Year-one target for a managed fleet: 30% reduction in downtime and 20% lower service costs. Projected — a target, not a measurement from the demo below.

Network-wide uptime

—

demo data

Open tickets, live

—

demo data

SLA compliance

—

demo data

Revenue vs. cost, monthly

—

demo data

Avg. downtime / device type

—

demo data

Technician productivity index

—

demo data

Parts spend, 3-mo. trend

—

demo data

Client satisfaction index

—

demo data

PM completion rate

—

demo data

Asset lifecycle remaining

—

demo data

See it work.

The same platform, seeded with a full fleet — 200 locations, 500 assets, eighteen months of history. Open it and click around.

Open the working demo