My Laser Doctor
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The doctor for your laser.

Text us a photo of the fault. We identify the exact machine, diagnose it, and walk your staff through the fix — and when a technician is genuinely needed, we book the visit. We service the machine, so you can treat the patient. Below is a real conversation, replayed.

My Laser Doctor · triage — Active now

This is an automated assistant from Laser Rx (not a person). Msg & data rates may apply. Reply STOP to opt out, HELP for help. Please send a photo of the equipment's label so we can identify it.

📷 [Photo of equipment label attached]

Reading the label…

Checking our device database…

Identified: Lumenis M22. Are you seeing Error Code 53 on the screen?

Yes, exactly. Error 53.

Error 53 is a cooling system fault. Have you checked the water level in the reservoir recently?

I don't know how to check that. I'm new here.

No problem. Look at the back of the machine. There is a transparent tube on the left. Is the water level between the two black lines?

It looks empty actually.

Got it — that's Error 53 with an empty reservoir, possible leak. This needs a technician on-site to inspect the cooling system.

Want me to call Lumenis for you?

Yes please!

Perfect! Calling them now. Give me like 10 minutes and I'll get back to you 👍

Checking your clinic's calendar for a good time…

Confirming a time with Lumenis dispatch…

Adding it to your calendar…

All set! 🎉 Got you booked with Lumenis for Thursday at 3:30 PM. I added it to your calendar and the tech knows exactly what to bring. Confirmation #LS-47392.

Recorded conversation — replayed, not live. Motion is reduced or not yet mounted, so the full exchange above is shown at once.

PHASE 1 · IDENTIFICATION

  1. 1. Staff Sends Photo
  2. 2. AI Decodes Image
  3. 3. Database Lookup
  4. 4. SMS Confirmation

PHASE 2 · DIAGNOSTICS

  1. 5. Staff Describes Issue
  2. 6. AI Diagnostic Q&A
  3. 7. Troubleshooting

PHASE 3 · DISPATCH

  1. 8. Request Technician
  2. 9. Confirm Request
  3. 10. Staff Confirms
  4. 11. AI Calls Mfr
  5. 12. Check Calendar
  6. 13. Book Technician
  7. 14. Add to Calendar

PHASE 4 · ANALYTICS

  1. 15. SMS Confirmation, logged

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.

The process, step by step

Every step below happens on a single text thread. Numbering is the actual order the platform runs in — nothing here is abstracted into vague benefit language.

  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.

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.

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, no more spreadsheets.

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

The numbers Jim's team watches every day

The proposal's own year-one projection: 30% reduction in downtime and 20% lower service costs. Projected — from the deck, not the demo below.

Network-wide uptime

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demo data

Open tickets, live

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demo data

SLA compliance

—

demo data

Revenue vs. cost, monthly

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demo data

Avg. downtime / device type

—

demo data

Technician productivity index

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demo data

Parts spend, 3-mo. trend

—

demo data

Client satisfaction index

—

demo data

PM completion rate

—

demo data

Asset lifecycle remaining

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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