JT

JDA TSG × BI Consulting Services

Customer Success & Managed-Operations Board · sourced from Airtable
Demonstration · Illustrative data · BI Consulting Services
Prepared for Max
September 2026
One board across every managed program Founded 2011 · US-based, SLA-first managed teams Technical Support · Customer Experience · Business Ops · Surge · AI Ops Airtable bases → Power BI dataflow → one semantic model
Signature view · program grid

Client-program health at a glance

One tile per managed program, coloured by composite health; the gauge is period NPS. Click a tile to pin it in the scorecard.

HealthyWatchAt riskHealth = NPS ≥ 75 · SLA ≥ 97.5% · backlog ≤ 1.0 days of volume
1
Source system · Airtable → Power BI

How four Airtable bases become one governed model

Record counts reflect the selected period. Airtable stays the operational system of record; Power BI owns the measures.

3
Volume

Forecast vs actual ticket / case volume

ActualClient forecastOver-forecast volume absorbed
4
Team utilisation & ramp

Seats staffed vs contracted

Dark bar = staffed core seats, light extension = surge seats drawn, tick = contracted.

5
Quality assurance

QA audit scores by program

Average of scored interactions against the client rubric; target line at 92.

6
Client-health scorecard

Program detail for the selected period

Sorted by health, then NPS. First-response figure is the period median in hours.

7
Ask the data · AI layer (mock)

Natural-language questions over the same semantic model

Which programs are trending below SLA and why?Where did surge seats go unused last quarter?Show NPS vs QA score by programDraft the Monday CS summary for Client E
8

Topline NPS anchored to JDA TSG's published LegalZoom case-study figure of 90%+ NPS (published case-study figure; all other values illustrative). Client programs are labelled generically (Client A–L) and are not JDA TSG's real accounts or results. Founded 2011; enterprise clients named publicly by JDA TSG include Microsoft, Intuit, LegalZoom and the NFL. Illustrative dataset generated for demonstration; every figure recalculates from the same program table so subtotals reconcile.

Also from BI Consulting Services

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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