7-Day Launch Checklist: Appliance Repair AI Front Desk — Portland, OR

This is a tight, practical 7-day plan to get an AI front desk ready for a Portland appliance repair business: build call flow, define services and rules, test real call scenarios, and go live. The goal is clear — answer every call fast, qualify correctly for first-visit success, and capture same-day, high-urgency jobs in neighborhoods from the Pearl District to Sellwood.

Day 1 — Map your service area and priority zones

Start with a clear geography. Portland’s market moves by neighbourhood and county: Downtown, Pearl District, NE Portland, SE Portland, Sellwood, and areas across Multnomah, Washington and Clackamas counties behave differently for same-day availability. Define the primary service radius and a secondary overflow radius.

Decide priority zones for same-day dispatch (for example: Downtown, Hawthorne, Alberta Arts District, Sellwood). These zones get first-available slots when techs are short; overflow areas get later windows. Mark locations like PDX, Mount Tabor and Forest Park as reference points for travel time estimates.

• List zip codes and core neighbourhoods you will serve same-day.
• Set max drive-time per tech (example: 30 minutes inside I-5 corridor, 45 minutes outside).
• Flag high-value clients (property managers, commercial kitchens) for priority routing.

Day 2 — Define services, triage rules and emergency flags

Create a concise service catalog that matches what your techs actually do in one visit: refrigerator cooling, gas range ignition, washer not draining, dryer not heating, ice-maker and water-line fixes, control board replacement, and installations. For each, record typical parts needed and whether it’s usually single-visit.

Write explicit triage questions to separate quick fixes from multi-part jobs. For high-urgency calls — refrigerator not cooling, gas smell, washer flooding — make emergency flags that push immediate priority and require an escalation step to on-call techs.

• For each service: define a one-line description, usual time-on-site, and likely parts.
• Triage examples: ask unit age, symptoms (no power, leaking, not cooling), and presence of gas smell.
• Emergency flags: refrigeration (food risk), flooding water loss, gas smell — these get highest priority.

Day 3 — Pricing rules, deposits and appointment windows

Decide how pricing is communicated during intake: give ranges (low/mid/high) and whether you quote a diagnostic fee or waive it with repair. For rentals and landlords, set rules on billing the property manager versus tenant and whether you require a tenant authorization.

Set booking rules: same-day windows, earliest arrival times, travel windows by zone, and cancellation/reschedule windows. These rules reduce admin friction and stop callers from expecting impossible arrival times during storm or holiday spikes.

• Define whether you provide price ranges on call or book a diagnostic first.
• Set deposit policy for same-day high-urgency jobs or replacement quotes.
• Create booking windows: same-day AM/PM slots, next-business-day, and after-hours options for emergency calls.

Day 4 — Create the call flow and handling scripts

Build a call flow that mimics a trained dispatcher: immediate pickup, friendly verification, targeted qualifying questions, emergency check, and booking or escalation. Keep it short and designed to capture the facts techs need: appliance type, model/age, symptoms, access instructions, photos/URLs and preferred appointment windows.

Draft short, natural sentences for each step so the AI sounds like your best dispatcher, not a script reader. Include explicit branches for landlord calls, property manager accounts, commercial kitchens and after-hours emergencies.

• Intake checklist for techs: appliance, symptoms, model/age, photos/URL, access window, gate or elevator instructions.
• Escalation path: if gas smell or fridge with food risk -> immediate on-call tech notification + priority flag.
• Landlord path: capture tenant contact, landlord billing info and permission to enter.

Day 5–6 — Test with real call scenarios and refine

Run a battery of live tests using realistic Portland call scenarios: tenant with a leaking washer on a rainy morning, landlord needing same-day fridge repair before a move-out, commercial kitchen with an oven failure during lunch prep, and a late-night call about a gas smell. Use actors or staff to role-play and listen to the intake notes that the AI produces.

Evaluate three things on each test: accuracy of the facts captured, proper emergency flags and routing, and whether the appointment windows and technician notes are usable without follow-up. Tweak the scripts, triage questions and booking rules where the AI misses key details or creates extra work for dispatchers.

• Test cases: refrigerator not cooling (urgent), washer overflowing, gas stove ignition fail, tenant-lockout scheduling.
• Check that intake captures photos/URLs and unit age every time.
• Confirm tech-ready notes: priority flag, parts likely needed, and clear access instructions.

Day 7 — Go live, monitor first-week calls and iterate

Flip to live and treat the first week as a controlled rollout. Have a human dispatcher shadow every AI call for quality control and to immediately intercept any false negatives on emergencies. Tally missed or incomplete intakes and correct the call flow fast.

After the first weekend (often your busiest), review the notes and job outcomes: Were parts right? Were returns reduced? Did same-day bookings increase? Use that review to tighten triage, adjust booking windows in busy neighbourhoods like Hawthorne and the Pearl District, and update your emergency escalations.

• Shadow every call for 48–72 hours and collect feedback from techs on intake quality.
• Adjust rules for high-traffic days (holiday oven spikes, wet-month dryer failures).
• Plan a weekly review cadence for the first month to reduce admin errors and improve first-visit success.

Questions appliance repair owners in Portland ask

Will an AI front desk handle gas-smell emergencies correctly?

Yes — if you configure explicit emergency flags on Day 2 and test them Day 5–6, the system will escalate gas-smell calls to your on-call tech immediately and mark the job as high priority. You should still have a dispatcher shadow initial live calls until you trust the escalation path.

Can it capture photos, model numbers and access instructions?

Configure the intake checklist to require model/age, symptoms and a photo or URL during calls. In tests, confirm that those fields are consistently captured and routed to techs; accurate photo capture alone reduces repeat visits and saves admin time.

How do I keep the personal touch for repeat customers and landlords?

Use a landlord/property-manager path in your call flow that captures account notes, billing details and preferred techs. You can script polite, familiar language for repeat customers during intake so calls still feel local and personal while remaining efficient.

What if the AI misses a detail or books the wrong window?

Treat the first week as supervised learning: have staff monitor calls and correct the flow immediately. Most issues are solved by tightening questions or changing appointment rules; the goal is fewer callbacks and cleaner intake, not a perfect first run.

How quickly can I hear the system in action?

You can hear a live demo almost instantly from the landing page. It’s the fastest way to check tone, flow and how emergencies are flagged.