From QuickBooks data to better profit decisions.

Click “Connect to QuickBooks” and you’re sent to Intuit’s official authorization screen, where you log in the way you always do and approve access for JobProfitAI. It takes about two minutes.

This uses Intuit’s standard OAuth connection, the same mechanism every QuickBooks app uses. Nothing about your QuickBooks credentials passes through our servers; we receive an access token, which we encrypt before storing.

JobProfitAI reads QuickBooks Projects (sub-customers) and pulls together everything attached to each one. Invoices, bills, expenses, purchases, time activities and estimates. Contractors who track job cost by Class instead are not supported yet; that is on the roadmap rather than in the product, and it is worth checking before you sign up.

Costs are grouped into categories you’d actually recognize: labor, materials, subcontractors, equipment and overhead. Where a cost was tagged to a customer rather than to the specific job, JobProfitAI attributes it only when it can do so defensibly, and flags that it made that judgment rather than hiding it.

Optionally, you set a company-wide target margin and an overhead allocation method. Both are yours to configure; neither is guessed at.

Your Profit Dashboard shows revenue, cost, gross profit and margin for every job, plus company-wide totals: average job margin, how many jobs are below your target, and the dollar value of profit currently at risk.

Open any job to see the full cost breakdown by category, the estimate-versus-actual comparison, and a profit leakage view showing where expected profit turned into actual profit.

“Needs Your Attention” is a deterministic rule set that runs across every job: costs running over estimate, margins below target, margin declining across recent weekly snapshots, and cost categories that look like outliers compared to similar completed jobs.

Each finding carries a dollar impact, a severity, and a confidence level. Confidence is based on how much data supports the finding, a pattern across six completed jobs is treated differently from a sample of one, and rules that need comparison data simply don’t fire until there’s enough of it.

There’s also a Data Health page that tells you exactly what is missing before you trust a number: expenses not tagged to any job, jobs with revenue but no costs recorded, time entries QuickBooks gave us with no hourly rate, and jobs with no cost estimate entered here. Those gaps change what the numbers can tell you, so they’re shown rather than papered over.

Profit Intelligence takes the patterns found across your jobs and turns them into plain-English findings: what was observed, the evidence behind it, the financial impact, and a concrete action worth considering.

An important detail about how this is built: the dollar amounts, percentages, confidence levels and the list of jobs behind each finding are all calculated by the application, and those are the values stored and displayed. The AI writes the explanation and the recommendation around them. Every finding names the jobs it came from, so you can check any of it against the job pages rather than taking it on faith. That’s deliberate. Profit decisions shouldn’t rest on a number a language model produced.

Once a week, on the day and hour you choose in your own timezone, JobProfitAI syncs the latest data and emails a short brief. It opens with what changed since the last one: new costs and billing on each job, margins that moved, jobs that just went over their estimate, and jobs you marked completed. That part is calculated, not written by AI. A short written summary follows, leading with the job that most needs your attention.

It goes to as many recipients as you like. You, your project manager, your bookkeeper. And if your data is too incomplete that week to say anything reliable about profitability, the email says exactly that and lists what’s missing, instead of writing a confident-sounding summary around gaps.