SAMPLE: Protis Global × BI Consulting Services

1Placements per month

Solid bars are the selected period; hollow bars are the same months one period earlier.

Selected periodPrior periodFees billed (line, $K)

2What this board answers

Three questions from the December 2024 discovery call, answered live.

3Placement flight map Delray Beach ↔ Chicago → client metros

Each arc is a placement route from the office running the search to the metro where the hire landed; arc weight = placements.

Delray Beach searchesChicago searchesClient metro (size = placements)

4Talent pipeline sourcing → placed

Candidates carried through each stage for searches closed in the period; band width = candidates.

5Fees & placements by practice

Retained + contingent fees billed in the period. Selecting a practice above highlights its row.

6Time-to-fill by practice days, signed search → accepted offer

Band = 25th–75th percentile; dot = median. Dashed line = firm-wide median for the period.

7Active searches by stage snapshot, 10 Sep 2026

Every open search, placed on its stage; darker = longer in stage. Amber ring = 30+ days in current stage.

8Recruiter productivity

Placements closed in the period, with active load today.

9Client repeat rate who comes back

A placement is “repeat” when the same client had at least one earlier placement with Protis. Top clients in the period, with first-placement year.

10Ask the data Copilot-style natural-language layer over the same model

Try asking

Which searches have been in client interviews longest?Fees by practice vs last yearMedian time-to-fill for VP roles in ChicagoWhich clients hired twice this year?Offer acceptance rate by recruiter

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.