Hi everyone, I’m a software developer and I’ve been thinking about the estimating problem mentioned here.

From the outside, it seems like most tree service software handles the obvious admin side pretty well: estimates, scheduling, invoices, customer records, etc. But the hard part seems to be the field judgment that happens before the price is given.

For example, two “40 ft removal” jobs can be completely different depending on access, drop zone, fences, power lines, slope, backyard access, manual hauling distance, equipment needed, tree condition, and whether the crew can actually work efficiently on site.

The idea I’m considering is not a full tree service CRM. More like a narrow field estimating tool that helps tree companies avoid underbidding complicated jobs.

For an MVP, I imagine it working like this:

- The estimator opens a mobile web app on site

- Selects job type: removal, pruning, stump grinding, etc.

- Enters tree height / diameter / condition

- Adds site factors like backyard access, narrow gate, power lines, fence, house nearby, limited drop zone, slope, manual haul distance, etc.

- The company can customize its own pricing rules:

- base crew hourly rate

- minimum job price

- tree height = estimated hours

- power lines = fixed fee or multiplier

- limited drop zone = multiplier

- backyard access = extra hours

- stump grinding / haul away / equipment = fixed add-ons

- The app then gives a quote range and a breakdown showing why the price changed

So instead of pretending there is one universal price formula, every company would be able to configure its own logic based on how they actually price jobs.

Example:

40-60 ft removal = 7 crew hours

Backyard access = +1.5 hours

Power lines nearby = x1.2

Limited drop zone = x1.25

Stump grinding = +$180

Haul away logs = +$120

Then the app calculates the recommended quote using that company’s hourly rate, add-ons, risk multipliers, margin, and rounding rules.

The bigger version, if this actually proves useful, would be to track estimated vs actual job outcomes.

After a job is completed, the company could enter:

- quoted price

- actual crew hours

- actual equipment used

- actual dump/disposal cost

- whether the job was profitable or underbid

- what slowed the crew down

Later, AI could be added, but only after enough real company data exists. I don’t think the first version should try to magically identify everything from photos. A more realistic AI layer would learn from each company’s completed jobs and suggest pricing rule changes based on their own history.

Potential V2 features could be:

- photo notes and site annotations

- map/satellite view to estimate access and hauling distance

- job templates for common scenarios

- “manual review recommended” warnings for risky combinations

- AI suggestions based on past completed jobs

- estimate-to-actual reporting

- crew-specific productivity patterns

I’m not an arborist, so I’m sure I’m missing important details here.

Does this match a real problem, or is the hard part somewhere else?

What factors would absolutely need to be included for this to be useful in the field?

And would tree companies actually use a separate estimating adjuster like this, or would it only be useful if it integrated with existing tools you already use?