SAMPLE: Stasis Drilling Solutions × BI Consulting Services

12-month probability-weighted rental revenue forecast

Every project in the pipeline is projected forward from its estimated start date: daily rental rate × expected on-rent days × probability of capture. The board replaces the Revenue Projection template and the historical tracker with one live view you can filter for shareholder updates — and every figure recalculates when you change a filter.

Probability band

Basin

Forecast horizon

Weighted vs. unweighted revenue by month

Bars = probability-weighted forecast · line = unweighted pipeline · shaded base = contracted backlog (100% probability)

Weighted forecastContracted backlogUnweighted pipeline

What this board answers

Three questions from the June 2024 conversation, answered from the current filter

Project timeline ribbon — start date × daily rate × probability

Each bar is one project: horizontal span = estimated start to end, bar height = daily package rate, shade = probability of capture. Hover a bar for the weighted revenue it contributes.

Weighted revenue by basin

Within the selected horizon

Pipeline by probability band

Unweighted value → weighted value, per band

Backlog vs. forecast

How much of the horizon is already contracted

Fleet utilisation grid — expected units on rent, by unit class and month

Illustrative 24-unit fleet: 6 Helix™ control systems · 8 Titan 5 RCDs · 6 Hydra-Balance choke manifolds · 4 Coriolis flow-meter skids. Cell = probability-weighted units on rent ÷ units in class.

Ask the data

Natural-language layer over the same model — a mock of the question Nathan raised in the original call

Question

Answer · generated from the forecast model

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.