The Walkthrough/Margin

Where Did the Margin Go? Know for Sure.

Watch Rafael take real job cost data, original estimates, change order logs, and project notes — and turn them into a ranked diagnosis of which jobs leaked margin, why, and what is still recoverable.

Watch the Walkthrough

What this guide covers

Why traditional services businesses struggle to diagnose margin leakage at scale
How AI can combine job cost summaries, estimates, change orders, and project notes
What a lightweight DIY workflow could look like using GPT or Gemini
How to categorize root causes: labor overrun, material escalation, scope creep, and more
Where the manual version breaks and what a more connected internal workflow could become

Margin Leakage Starter Kit

Get the companion asset for this guide and try a lightweight version of the workflow yourself.

What's inside

Master analysis prompt
Pattern view prompt
Data prep checklist
Root cause taxonomy
Example output table

Ready to get started?

Key takeaway

Most margin reviews stop at the variance column. AI becomes useful when it connects the numbers to the narrative — matching cost overruns to field notes, change order gaps, and estimating assumptions to produce an actual diagnosis.

What the output could look like

Job	Variance	Root Cause	Evidence	Recoverable
Memorial Hospital Chiller	−$47,200	Scope creep	PM note 2/14: "Added drain pan work per owner request." No signed CO in log.	Yes — $38K if CO raised
Riverside Office TI	−$31,500	Labor overrun	Actual labor 1,240 hrs vs. bid 880. Field note: "Rework on ductwork installation."	No
District School HVAC	−$22,800	Schedule delay	PM note 3/2: "AHJ held us up 3 weeks waiting on inspection." Premium labor to compress.	No

Example data shown. Actual output will be based on your job cost data, estimates, and project notes.

Want help turning this into a real internal workflow?

We help field-service and legacy businesses implement AI workflows that connect to your existing systems.