SAMPLE: Worksystems × BI Consulting Services

Workforce Outcomes Board — Portland Metro, from enrolment to a paycheque

One governed Power BI model for the WorkSource Portland Metro system: participants, placements, wages, credentials and WIOA funding burn — sliceable by county, funding stream and programme year, with the report-health strip your deployment pipeline would feed.

Prepared for Trace September 2026 Scope: Multnomah & Washington counties · City of Portland Tagline nod: “Because Work Matters”

Honest footnote first. It has been about 15 months since you chose another contractor for the permissions, deployment-pipeline and DAX work. No hard feelings — this page is simply what we would have built as a working starting point, so if that project ever needs a second pair of hands, you can see the shape of it in five minutes.

Monthly enrolments vs job placements

Selected period highlighted; prior months shown muted for context. Placements are counted in the quarter after exit, as in your annual report.

EnrolmentsPlacementsPlacement rate (right axis)

Participant journey

Enrolled → in training → credential → placed → retained 2 quarters

WorkSource Portland Metro — placements by centre

Marker area is proportional to placements in the selected filters. Centres are illustrative service points across the two-county area.

250 placements1,0002,000— Willamette / Columbia shown for orientation

Training completions by provider

Occupational-skills completions; bar = completions, tint = credential attainment rate

Funding stream burn vs allocation

Spend to date against programme-year allocation; the tick marks the straight-line expectation for the period.

What this board answers

Three questions a board member or a funder asks — answered from the current filter

Ask the data

Natural-language layer over the same governed model

Which centre has the biggest gap between training completions and placements?

Report health

The strip a deployment pipeline (Dev → Test → Prod) writes on every refresh — the part of the original ask around DAX and pipelines.

Private AI on your own server

The "ask the data" panel above is built to run on a GPU server inside your own network: an open-weight model over your own documents, with every answer citing the file it came from. No per-seat licence, works offline, and nothing leaves the building.

How it sits in your environment

One appliance-class server on your rack or in a secured closet. Your files are indexed where they already live; the model runs locally; answers come back with the source page attached.

What that means in practice

1
Your data only, on your hardwareAn open model over your own documents and exports. No third-party AI processor, no data-transfer agreement to review.
2
Every answer cites its sourceReplies point to the file and page they were drawn from, so people check the answer instead of trusting it blind.
3
No per-seat licenceThe whole team can ask questions. Cost is the server and our build, not a monthly number that grows with headcount.
4
Works offlineIf the internet connection goes down, the answers still come. Nothing depends on a vendor's uptime or pricing changes.