Experience Analytics in Action
Explore live dashboards, automation, and AI-powered business insights.
Every industry, its own dashboard story
Twelve industry dashboards. Different KPIs, different business stories, one premium analytics experience. Explore interactive filters, charts, tables, and Executive Insights using demonstration data.
Real Estate dashboard
Balance occupancy, rent growth, and operating expense across the portfolio.
Sample dataOccupancy
92.4%
+1.8ppNOI (TTM)
$48.6M
+6.2%Avg rent / sqft
$32.40
+3.1%Days on market
41
-6dOccupancy by portfolio
Occupancy vs target 93%
92.4%
Target 93.0%-0.6%
- Actual
- 92.4%
- Target
- 93.0%
- Gap
- -0.6%
NOI mix by portfolio
Click to filterNOI by market
Click to filterLeasing funnel
OpEx ratio (portfolio × market)
| Dallas | Austin | Phoenix | Denver | Atlanta | |
|---|---|---|---|---|---|
| Office | 38% | 36% | 42% | 39% | 41% |
| Retail | 34% | 32% | 36% | 35% | 37% |
| Industrial | 22% | 20% | 24% | 23% | 25% |
| Multifamily | 30% | 28% | 32% | 31% | 33% |
Top assets by NOI
Click to filter| Asset | Occupancy | NOI | Cap Rate |
|---|---|---|---|
| Uptown Tower | 96.4% | $4.8M | 6.1% |
| Legacy West Retail | 94.1% | $3.6M | 6.4% |
| Southport Logistics | 98.2% | $3.1M | 5.8% |
| Riverside Apartments | 95.2% | $3M | 5.6% |
Executive Insights
What the numbers mean for this business
- InsightOffice leads at $14.8M — 30% of the mix.
- RiskLargest funnel drop-off: Tours → Applications at 59% — focus retention here.
- RiskOccupancy vs target 93% is off target by 0.6 — worth reviewing.
See what your data could reveal
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Explore technology examples
These sample tools illustrate dashboard, spreadsheet, workflow, and automation techniques using demonstration data.
A live view of the metrics that move the business
KPI cards, revenue trends, customer segmentation, sales by region, and operational health — all interactive.
Revenue
$5.6M
Customers
21,130
Churn rate
3.1%
Uptime
99.94%
Revenue trend
Last 12 months · AllCustomer segmentation
Sales by region
Operational metrics
Avg. response
1.4h
-22% wk/wk
Orders / day
3,214
+8.6%
Fulfilment SLA
97.2%
+1.1pp
Error rate
0.18%
-0.04pp
Sales & Financial dashboard
Interactive filters, drill-down, and a semantic model powering executive KPIs — the way a real Power BI report feels.
Fictional sample data · for demonstration only
FY26 · All regions · USD
Revenue
$24.1M
+18.4%
Gross margin
62.8%
+2.1pp
New logos
184
+12
Pipeline
$38.6M
+22%
Revenue vs. forecast
Revenue by product
Click a row (Power BI drill-through)
Automated reporting workbook
Power Query pulls, cleans, and refreshes the model. Pivot-driven KPI cards and charts sit on top — one click to update.
Fictional sample data · for demonstration only
| Region | Units | Revenue | Margin |
|---|---|---|---|
| North | 4,820 | $128,400 | 42.0% |
| South | 3,650 | $98,700 | 38.0% |
| East | 5,940 | $156,800 | 45.0% |
| West | 6,720 | $182,300 | 48.0% |
| TOTAL | 21,130 | $566,200 | — |
Revenue by region
PivotChartSum
$566,200
Avg margin
43.3%
Best region
West
Executive & operational storyboard
Combined executive KPIs, operational drill-down, and geographic mix — the Tableau storytelling pattern our clients ship to the board.
Fictional sample data · for demonstration only
Quarterly performance
Filters
Insight
Q4 revenue up 76% vs. Q1; enterprise segment drives 58% of growth.
Visual ETL workflow
Two source systems, cleansed and joined, validated, then delivered to a warehouse and a governed Excel deliverable.
Fictional sample data · for demonstration only
Rows in
1.2M
Rows out
1.18M
Rejected
0.9%
Runtime
42s
Automated data-processing workflow
A scheduled Python job that ingests raw files, cleans and analyzes, then generates the executive report — no human in the loop.
Fictional sample data · for demonstration only
import pandas as pd
from pathlib import Path
from report import build_workbook, email_report
df = (pd.read_csv("s3://raw/sales.csv")
.dropna(subset=["order_id"])
.assign(revenue=lambda d: d.qty * d.unit_price))
summary = (df.groupby(["region", "product"])
.agg(revenue=("revenue", "sum"),
orders=("order_id", "nunique"))
.reset_index())
wb = build_workbook(summary, title="Weekly Sales")
email_report(wb, to="exec@client.com")
Pipeline run · #4,218
Scheduled Monday 06:00 CT
Records
248,912
Runtime
1m 12s
Status
Sent ✓
Model your savings in real time
Slide the inputs. Watch payback, cumulative savings, and hours recovered update instantly.
Your inputs
Illustrative model assuming 75% automation of manual reporting. Actual results depend on process, data quality, and scope.
Annual savings
$101.4K
Hours recovered
1,560
Year-1 ROI
576%
Cumulative savings vs. investment
Break-even in month 2
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From spreadsheet chaos to AI-ready insights
Watch the pipeline flow — every stage automated, governed, and observable.
Interactive sample pipeline
Now processing
Excel
Messy sheets & CSVs
Rows processed
2.4M
Latency
1.2s
Quality score
99.6%
Illustrative demonstration values