- Customer sends photos by text or email.
- Team guesses volume, access, and pickup needs from the photos.
- Team asks follow-up questions if key details are missing.
- Estimate is given with a note that onsite verification may change price.
- Pickup time is scheduled after the customer confirms.
Sample Deliverable
Photo quote intake audit.
A simple example of what a local service business gets from the $250 AI Workflow Audit. This sample uses a generic junk removal / field-service quote flow and does not use private customer data.
Workflow picked
Customer texts photos for a quote, then the team verifies details and schedules pickup.
The goal is not to replace the owner or dispatcher. The goal is to make the first customer message complete enough that the estimate, follow-up, and scheduling step do not get stuck.
- Photos often miss stairs, distance from truck, or item scale.
- Customer may not send address, preferred pickup window, or access notes.
- Estimator has to retype the same questions.
- Customer can go cold before the quote is clear.
AI-assisted fix
Use a short intake script before the estimate.
Thanks. To quote this accurately, send:
- 2-4 photos from different angles
- Pickup address or nearest cross streets
- Where the items are located: curb, driveway, garage, basement, upstairs
- Any access issue: stairs, elevator, narrow gate, long walk
- Preferred pickup day/time
Once I have that, I can send a tighter estimate and available pickup windows.
The team stops improvising the same follow-up questions. Customers send better info up front, estimates become clearer, and scheduling can happen faster.
The owner still reviews edge cases, sets the price, and confirms the schedule. The AI-assisted part just standardizes intake and drafts the next message.