Higley Project Controls Board

01 · Earned revenue vs plan

Monthly earned revenue against the baseline schedule of values

$ millions per month · earned = percent-complete × contract, plan = baseline S-curve

EarnedPlan

02 · Four-state footprint

Projects in flight, sized by contract value

Cleveland HQ, Akron, Detroit, Pittsburgh and Warren NJ offices · hover a marker for job detail

$12M → $84M contract Trending over budget Higley office

03 · Backlog & fee by market

Remaining contract value and projected fee

Active jobs · backlog = contract − earned to date

04 · Job cost vs budget

Estimate-at-completion variance to budget

Active jobs · red = projecting over budget, green = under

05 · Schedule health

Projects in flight — baseline finish vs forecast finish

Bars run from notice-to-proceed to baseline substantial completion; the hatched extension is forecast slip

BaselineForecast slipToday (Sep 2026)

06 · WIP schedule

Work-in-progress by job

Top jobs by contract in the current filter · the same rows the monthly WIP report is built from

What this board answers

Three questions from the October discovery call

Ask the data · private AI over the model

Natural-language questions, answered from the same tables

Runs against Higley's own semantic model — nothing leaves the tenant.

Which jobs are trending over budget this quarter, and by how much?

Behind the board

How it would be built

A Power BI semantic model over the job-cost, billing and schedule tables in Higley's primary business software, refreshed nightly through a gateway. One model, three pages (executive, project controls, per-job), row-level security by office. Filters here mirror the slicers that would ship.

Private AI on your own server

The "ask the data" ideas on this board are designed to run on a GPU server inside your own network — an open-weight model over your own documents, cited answers, no per-seat licence, nothing leaving the building. The short deck below explains how it works. Scroll through, or download it as a PDF.