DEMONSTRATION · ILLUSTRATIVE DATA · BI Consulting Services

AnalytIQCore · Executive Director's monthly board

The Village at Orchard Ridge x BI Consulting Services

One campus, seven care levels, read the way an Executive Director runs the month: independent living sales and move-ins against target, occupancy by residence and care level, the health center's census and staffing, and how residents feel about all of it. Winchester, Virginia, a Life Plan Community of National Lutheran Communities & Services.

AnalytIQCore demonstration
Prepared for Denise
September 2026

The month at a glance

Panel 1 of 9

1

Campus plan: every residence, this month

Panel 2 of 9

Each square is an apartment, each house a cottage, each lot on the Laurel circle a build-to-suit home, and each cell in the health center a licensed suite or bed. Click any area of the plan to filter the board to it.

Occupied Vacant or available Laurel home reserved, in build Laurel lot available Selected on the filter
2

Twelve-month trend: move-ins against target, and occupancy

Panel 3 of 9

Move-ins or admissions Monthly target Occupancy (right axis)
3

Sales pipeline

Panel 4 of 9

4

Lead sources

Panel 5 of 9

SourceInquiries and referralsShare / moved in
5

Occupancy by residence type and care level

Panel 6 of 9

Residence or care levelCapacityOccupiedOccupancyMove-insMove-outsNet12-mo move-ins vs targetWould recommend
6

Health center: census and staffing

Panel 7 of 9

Assisted Living, Memory Care and Skilled Nursing, with nursing hours per resident day against each level's staffing target, agency and overtime share of worked hours, and open positions.

7

Resident satisfaction and wellbeing

Panel 8 of 9

Would recommend, by residence and care level, this month

8

Ask the data

Panel 9 of 9

Three questions typed the way Denise would ask them. Each answer is generated from the numbers already on this board, for the filters set above, and names the panel it came from so anyone can check it.

Private AI on Orchard Ridge's own server The model runs inside the community's own network against this board's data. Nothing leaves the building: no resident, census, staffing or sales figure is sent to an outside AI service.
Denise asked
AI
Denise asked
AI
Denise asked
AI

Automation: three things the team stops doing by hand

Built on the same data

Each one runs on the feeds behind this board. Hours are illustrative monthly estimates for a single-campus team, and each card shows what it would have produced for the filters above.

Automation 1

Month-end census and occupancy roll-up

What it replacesPulling the health center census from the clinical record and the independent living roster from the resident system, reconciling move-ins, move-outs and transfers by residence type in a spreadsheet, then retyping the totals into the board packet.

12hours saved a month
Automation 2

Lead source and paid search attribution

What it replacesExporting inquiries from the sales CRM and clicks from the ad account every week, matching each name to a tour, deposit and move-in by hand, and building the source table for the sales meeting.

10hours saved a month
Automation 3

Daily nursing hours and agency exception alert

What it replacesBuilding the hours per resident day sheet from timekeeping and census each morning, and reading agency invoices line by line to spot which level of care is buying shifts at a premium.

14hours saved a month
Together, time handed back to the Executive Director, sales and nursing leadership36 hours a month, about 432 a year