Demonstration · Illustrative data · BI Consulting Services
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Portfolio Ledger — QMS, version two.

The QMS model already running in your Power BI tenant scores every project on CPAR, QQCCI, PPP and the Employee Thermometer. This board joins those scores to the Controller's numbers — funded value, period of performance, burn vs plan, revenue by agency, indirect rate and unbilled AR — through the same Project Name Mapping dimension, so one filter drives both.
Prepared for MeghanSeptember 2026
Standardized reporting · v2 concept

Where the portfolio sits

Contracts and funded value by region

Trend · 24 months

Monthly revenue recognized vs. plan

Revenue recognizedPlan (funded ÷ months of PoP)Selected period

What this board answers

Three questions a Controller asks at month-end

Signature view · Period-of-performance burn ribbons

Funds burned vs. time elapsed, by contract

Funds burnedRemaining funded valueToday (Sep 2026)▲ overburn >5pp▼ underburn >5pp
Contract · agency
Period of performance (Oct 2022 → Sep 2030)
Burned vs elapsed
Table view · all contracts in this view

Breakdown · by client agency

Revenue in period, with portfolio QMS score

QMS scores · the model you already have

CPAR · QQCCI · PPP · Employee Thermometer

AI layer

Auto-drafted month-end note & ask-the-data

YOU ASKED ·
Answers are computed from the filtered model on this page · in production: Copilot / Fabric AI skill over the DexisOnlineDataModel semantic model, grounded in the same measures.
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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.

Private AI Servers — slide 1 of 14
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Private AI Servers — slide 14 of 14

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