Scoop
Domain Intelligence
AI is powerful.
Without context, it's blind.
Your best people know what to look for in the data — which patterns matter and which are noise. Domain Intelligence captures that judgment and applies it everywhere, automatically.
Every business has that one person who just knows.
We sat in a room with a COO and his best operators at a national retail chain. We watched them look at dashboards and argue about what the numbers meant. One person would see a spike in inventory and shrug — normal seasonality. Another would see the same spike and say "that store is about to crater in six months."
Same data. Completely different conclusions.
The difference was context. Twenty years of pattern recognition that wasn't in any dashboard. We needed to capture that.
"There's one person in our organization who can look at these reports and see what's going to happen in six months. We have 1,200 locations. He can't get to all of them. We're trying to scale that person."
Everyone has AI. Nobody has context.
AI can query data and build charts. But it doesn't know what "doing well" means in your business. That tribal knowledge lives in your best people's heads — and nowhere else.
Answers the question you asked.
Doesn't know if it's the right question. No business context. Probabilistic guessing.
Shows what happened.
Humans still have to interpret and investigate. Can't explain why. Can't prioritize.
Knows what to look for. Investigates autonomously.
Prescribes what to do and why — like your best person would. Repeatable, governed, trustable.
Here's what the AI actually delivers.
No human wrote this. The system screened every location, investigated the flagged ones, and generated a finished report — fully automated.
What happens when a location gets flagged
The AI runs 15+ probes, then decides if it needs to dig deeper. Different locations get different investigation trees based on their specific issues.
flowchart TD
DSD["Deep Diagnosis — 15 probes"] --> P1["Revenue Trends
Accelerating decline"]
DSD --> P2["Pricing Analysis
At restrictive threshold"]
DSD --> P3["Trend Direction
Worsening month-over-month"]
DSD --> P4["Aging Assets
+82% YoY in stale bucket"]
DSD --> P5["Category Breakdown
Core categories collapsing"]
DSD --> P6["Customer Segments
Loyal customers leaving"]
DSD --> P7["Margin Analysis
Crashed from 29% to 8%"]
DSD --> ML["ML Root Cause
Finds the #1 predictor
humans would miss"]
DSD --> P8["...more probes"]
DSD -->|"AI decides: need deeper analysis"| LI["Follow-up Investigation
3 targeted probes"]
LI --> LP1["Pipeline sustainability gap"]
LI --> LP2["Volume + size both declining"]
LI --> LP3["Contraction timeline"]
style DSD fill:#FEF1F5,color:#130417,stroke:#E3165B
style P1 fill:#fff,color:#130417,stroke:#E5E7EB
style P2 fill:#fff,color:#130417,stroke:#E5E7EB
style P3 fill:#fff,color:#130417,stroke:#E5E7EB
style P4 fill:#fff,color:#130417,stroke:#E5E7EB
style P5 fill:#fff,color:#130417,stroke:#E5E7EB
style P6 fill:#fff,color:#130417,stroke:#E5E7EB
style P7 fill:#fff,color:#130417,stroke:#E5E7EB
style P8 fill:#fff,color:#130417,stroke:#E5E7EB
style ML fill:#FEF1F5,color:#130417,stroke:#E3165B
style LI fill:#FEF1F5,color:#130417,stroke:#E3165B
style LP1 fill:#fff,color:#130417,stroke:#E5E7EB
style LP2 fill:#fff,color:#130417,stroke:#E5E7EB
style LP3 fill:#fff,color:#130417,stroke:#E5E7EB
Top-line findings with data — what's happening and where
Per-location diagnosis — what the AI found when it dug in
How the AI traced a problem from symptom to root cause
Specific, data-referenced recommendations
Exactly how the AI reached each conclusion
Executive Summary
Three locations exhibit operational distress — costs growing 12–18% while same-store revenue declines up to 9%. Customer acquisition has diverged sharply across locations.
Critical: All three show costs outpacing revenue — capital is getting locked in operations that aren't converting. Location B is most concerning: traffic barely growing while revenue declined 9%.
| Location | Traffic | Revenue | Costs |
|---|---|---|---|
| Location A | +8% | -4% | +12% |
| Location B | +3% | -9% | +15% |
| Location C | +10% | -2% | +18% |
Key Findings
Systematic cost overrun: All locations show costs growing 12–18% while revenue declines, indicating an operational efficiency problem across the group.
Location A: Volatile traffic (3K–15K monthly), improving conversion (65%→80%), but losing mid-tier repeat customers (Silver -20%, Gold -24%).
Location B: Conversion crisis — rate dropped from 38% to 26%. Highest-value customers down 18%.
Location C: Returns surging across categories — up 178% in one segment, 485% in another — despite strong average transaction values.
Location B — Discovery Path
After 22 consecutive months of growth (17–70% YoY), same-store revenue turned negative for the first time.
Conversion rate dropped from 38% baseline to 26% — fewer visitors are converting despite stable traffic.
Customer segmentation identified the source: highest-value tier down 18%, entry-level tier down 75%. Total customer spending declined from $322K to $282K.
Category analysis showed the primary segment declining 18% YoY while secondary segments held — suggesting targeted rather than systemic issues.
Recommended Actions
- Location B conversion audit — conversion dropped from 38% to 26%; investigate pricing, merchandising, and competitive pressure
- Customer retention program — mid-tier repeat customers churning at Location A despite improving conversion; segment analysis needed
- Returns investigation at Location C — 178–485% increases suggest quality, sizing, or expectation mismatch
- Cost structure review — all three locations show costs outpacing revenue; identify which categories are driving divergence
Investigation Methodology
1. Universal Screening — Screened all locations against revenue trends, cost ratios, conversion rates, and customer metrics.
2. Threshold Flagging — Identified locations where costs growing faster than revenue, conversion declining >15%, or segments shifting >20%.
3. Multi-Dimensional Diagnostic — 24-month analysis across traffic, segmentation, category performance, and operations.
4. Pattern Recognition — Identified inflection points, leading indicators, and peer comparisons.
5. Insight Synthesis — Actionable findings, open questions, and risk prioritization as executive narratives.
Same engine. Your context. That's the point.
The technology is the same. What changes is the domain intelligence — the context that makes AI useful in your specific business.
A national retail chain found systemic patterns no store visit would have revealed.
Multiple screening lenses run in parallel across every location. The leading indicators lens catches problems before they hit the bottom line — customer acquisition falling faster than existing revenue, meaning further decline is coming. ML discovered that customer loyalty tier was the #1 predictor of year-over-year change across an entire region. That's a systemic insight, not a store-level problem.
140 hotels, 5 revenue managers — every property gets the same investigative depth.
The engine screens every property against RevPAR balance, booking pipeline health, and segment performance. When a property gets flagged, it runs deep diagnostics — occupancy trends, rate vs comp set, guest segment shifts, channel mix. The ML engine automatically tests every guest segment — construction, medical, energy, corporate — across all properties.
Not a dashboard. Not a chatbot.
A governed investigation engine.
Six stages — from raw data to executive reports. AI doing real work in a structure that's trustable, on trustable metrics.
flowchart LR
A["Your Locations"] --> B["SCREEN
Multiple analytical
lenses in parallel"]
B -->|"Flagged"| C["INVESTIGATE
Deep diagnosis
15+ probes each
+ ML root cause"]
B -->|"Passed"| D["SAFETY NET
Health checks
AI escalates
if it finds something"]
C --> E["SYNTHESIZE
AI writes narratives
Root cause + action items"]
D --> E
E --> F["ROLLUP
Location → District
→ Region → Executive"]
F --> G["REPORTS
Client-ready reports
at every level"]
style A fill:#F5F5F5,color:#130417,stroke:#D1D5DB
style B fill:#FEF1F5,color:#130417,stroke:#E3165B
style C fill:#FEF1F5,color:#130417,stroke:#E3165B
style D fill:#fff,color:#130417,stroke:#D1D5DB
style E fill:#FEF1F5,color:#130417,stroke:#E3165B
style F fill:#fff,color:#130417,stroke:#D1D5DB
style G fill:#FEF1F5,color:#130417,stroke:#E3165B
Sits on top of your stack — not instead of it.
Domain Intelligence adds an investigation layer that makes everything you've already built more useful.
The insight: Copilot and Cortex are AI wrappers for your data. Scoop is an expert system that orchestrates analysis the way your best analysts would — automatically, at scale.
We've figured out 95% of it.
Let us do the last mile with you.
The AI doesn't learn your business on its own. Your people teach it, through us.
We sit with your best people and learn how they think. What they look for, what triggers concern.
We turn that into structured investigation contexts — screening lenses, probes, pattern rules.
We run it on your real data. Your team reviews. "I didn't see that" is the best feedback.
Once tuned, it runs autonomously — every location, every cycle. Your people review and act.
"We don't come in claiming to know your business. We come in knowing how to learn it — fast."
Let's find out what your best people know.
A conversation with your operators. A pilot on real data. First reports in weeks — not months.
AI is powerful. Without context, it's blind. Let's give it yours.