7-Day AI front-desk launch checklist for Portland window washing companies

Stand up an AI front desk in a week that answers calls like a trained employee and keeps routes full. This checklist walks a Portland window washing owner through seven days of concrete setup: call flow, services and pricing rules, service-area limits, emergency handling, booking rules, scenario testing and going live.

Day 7: Gather the facts your front desk needs

Collect the operational facts the AI will use: service list, price bands, equipment notes, service-area boundaries and crew hours.

For Portland, list neighbourhoods you serve (NE, SE, NW, Downtown, Pearl District, Sellwood-Moreland, Lake Oswego, Columbia Corridor) and note county limits only when they affect travel or insurance.

Define what triggers an onsite estimate versus a standard job and what counts as high-reach.

• Primary services and common add-ons.
• Service-area hard cut-offs.
• Price/estimate triggers by height, window count, staining.
• Crew hours and same-day booking windows.

Day 6: Build the call flow and qualification script

Design a simple path: identify caller type (homeowner, property manager, commercial), urgency, job complexity and desired service.

Add explicit questions to flag high-value/high-risk calls (storefronts, second-story/high-reach, screens, hard-water).

Create canned responses for insurance, arrival windows and cancellation/reschedule policies.

• Opening script: greet, confirm service area, identify caller type, check urgency.
• Qualification triggers: property manager = escalate; high-reach = onsite estimate.
• Upsell prompts: screens, hard-water treatment.
• Confirmation: text booking details to customer and crew.

Day 5: Define booking, cancellation and emergency rules

Turn scheduling decisions into deterministic rules: minimum travel times, same-day cutoffs, cancellation windows and deposit rules for risky jobs.

Create an emergency-handling path for urgent storefronts and property-manager requests that tags and escalates appropriately.

Specify when the AI must escalate to a human (complex bids, insurance/permit questions).

• Same-day booking window and travel buffer rules.
• Deposit rules for high-reach or commercial contracts.
• Urgent escalation path for storefronts/property managers.
• Onsite estimate triggers and dispatcher review blocks.

Day 4: Prepare the data handoffs — texts, calendar rules and notes

Decide how intake becomes work: standardized job-card texts with address, service, urgency, special instructions and caller contact.

Define calendar rules for selecting slots, preventing double-booking and grouping routes with buffers.

Standardize checkboxes in notes and require photos for unclear or high-risk jobs; set confirmation cadence (booking text + 24-hour reminder).

• Text format: address | service | urgency | notes | caller phone.
• Calendar rules: buffer times, route grouping, max stops per day.
• Photo request step for high-risk jobs.
• Two-point confirmations to reduce no-shows.

Day 3: Build and test real call scenarios

Run 20+ real-sounding scripts reflecting Portland realities: storefront emergency before an event, post-storm streaking, property-manager recurring requests, Lake Oswego high-reach jobs.

Test with your actual phones and crews; have dispatchers review incoming job cards and log failures.

Iterate rules and rerun failing scenarios until classification, booking accuracy and handoffs are consistent.

• Test scripts: storefront emergency, property-manager recurring, high-reach with staining, simple one-off.
• Metrics: correct classification, booking accuracy, complete text content.
• Repeat and fix failed scripts until reliable.

Day 2 & Day 1: Staff training, go-live and first-week monitoring

Train dispatchers and crew leads on job-card format, escalation checklists and edit ownership for reschedules or changes.

Go live in a controlled window (start Monday with full dispatch). Route calls to the AI with a human monitor and override for the first 48 hours.

During week one, track every booking for correctness, rule application and text completeness; review logs after seven days and lock in fixes.

• Dispatcher checklist and escalation contact for AI-related issues.
• 48-hour human monitoring and override capability at launch.
• Track correctness of booking, applied rules, and text contents.
• Weekly review to fix common failures and adjust prompts.

Questions window washing owners in Portland ask

Will the AI handle property manager calls differently?

Yes. Flag property manager calls as high priority so the AI captures contract details, billing preferences, access instructions and routes the job for immediate dispatch review.

How do I prevent the AI from booking an impossible time slot?

Define strict calendar rules and travel buffers (earliest start, max stops, travel times between areas) and lock them during testing so the AI cannot override without approval.

What about emergency storefront cleans before events?

Create an 'urgent storefront' path that escalates to a human, tags the job as urgent in texts, and surfaces it to dispatch for same-day handling if possible.

How do I handle calls that need an onsite estimate?

Set clear triggers for onsite estimates (high-reach, heavy staining, multi-unit) and have the AI schedule the estimate appointment and note the reason in the job card.

Can I test the AI with real Portland call scenarios before going live?

Yes. Run 20+ test calls that mimic Portland realities (pollen season, post-storm, property manager requests, high-reach jobs) using your phones and dispatchers to catch misclassifications before full launch.