Experience Analytics in Action
Explore live dashboards, automation, and AI-powered business insights.
Every industry, its own dashboard story
Ten dashboards. Different KPIs, different chart types, one interaction model. Filters cross-update every KPI, chart, table, and AI insight instantly. Fictional demonstration data.
Logistics dashboard
Move more packages on time at lower cost per mile. Watch where routes and warehouses slip.
On-time delivery
94.2%
+1.3pp
Cost per mile
$1.84
-3.1%
Fleet utilization
78.0%
+4.2pp
Shipments / day
12,480
+8.6%
On-time delivery trend
Fleet utilization vs target 80%
78.0%
Target 80.0%-2.5%
Shipments by region
Click to filterWarehouse throughput (hour × weekday)
| 6a | 9a | 12p | 3p | 6p | 9p | |
|---|---|---|---|---|---|---|
| Mon | 620 | 780 | 720 | 540 | ||
| Tue | 640 | 810 | 760 | 560 | ||
| Wed | 660 | 830 | 780 | 580 | ||
| Thu | 680 | 860 | 800 | 600 | ||
| Fri | 520 | 720 | 910 | 860 | 640 | |
| Sat | 540 | 520 |
Cost per shipment by service
Click to filterDelay reasons (Pareto)
Top lanes performance
Click to filter| Lane | Shipments | On-time | Cost/mi |
|---|---|---|---|
| DFW → ATL | 4,820 | 95.4% | $1.72 |
| LAX → PHX | 3,640 | 96.1% | $1.68 |
| EWR → BOS | 2,980 | 93.2% | $1.94 |
| ATL → MIA | 2,540 | 92.8% | $1.88 |
AI insights
- Traffic leads at 320 — 33% of the mix.
- Other trails the leader by 5.3× — highest-leverage growth target.
- Fleet utilization vs target 80% is off target by 2.0 — worth reviewing.
Need a dashboard designed for your business?
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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)
Need something similar for your business?
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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
Need something similar for your business?
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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.
Need something similar for your business?
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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
Need something similar for your business?
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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 CST
Records
248,912
Runtime
1m 12s
Status
Sent ✓
Need something similar for your business?
30 minutes. No pitch. We'll map the fastest path to a dashboard like this one.
Model your savings in real time
Slide the inputs. Watch payback, cumulative savings, and hours recovered update instantly.
Your inputs
Assumes ~75% automation of manual reporting. Real engagements typically hit 70–90%.
Annual savings
$101.4K
Hours recovered
1,560
Year-1 ROI
576%
Cumulative savings vs. investment
Break-even in month 2
Want a detailed report tailored to your team?
From spreadsheet chaos to AI-ready insights
Watch the pipeline flow — every stage automated, governed, and observable.
Live pipeline preview
Now processing
Excel
Messy sheets & CSVs
Rows processed
2.4M
Latency
1.2s
Quality score
99.6%
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