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The investigation layer that encodes your best operators' judgment and runs it across every location, automatically. It knows which patterns matter — and which are noise.
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 — and make it run at scale.
"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."
Power BI Copilot and Snowflake Cortex can answer the questions you ask. Domain Intelligence decides which questions to ask, runs the investigation autonomously, and delivers the findings — like your best analyst would.
Doesn't know if it's the right question. No business context. No governed investigation.
Humans still interpret and investigate. Can't explain why. Can't prioritize what matters.
Prescribes what to do and why — like your best person would. Repeatable, governed, trustable.
No human wrote this. The system screened every location, investigated the flagged ones, and generated a finished report — fully automated.
The AI runs 15+ probes, then decides whether to dig deeper. Each investigation tree adapts based on what it finds — the same way your best analyst actually thinks.
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flowchart TD
DSD["🔍 Deep Diagnosis
15+ targeted probes"]
DSD --> G1["Revenue & Pricing
Trends"]
DSD --> G2["Customer &
Segment Analysis"]
DSD --> G3["Category &
Operational Data"]
DSD --> ML["🧠 ML Root Cause
Finds the #1 driver
humans miss"]
G1 & G2 & G3 & ML --> SYNTH["📋 AI Synthesizes
Root cause + action items"]
DSD -->|"AI decides: need deeper look"| FU["Follow-up Investigation
3 targeted probes"]
FU --> SYNTH
style DSD fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
style G1 fill:#fff,color:#130417,stroke:#E5E7EB
style G2 fill:#fff,color:#130417,stroke:#E5E7EB
style G3 fill:#fff,color:#130417,stroke:#E5E7EB
style ML fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
style FU fill:#F3F4F6,color:#130417,stroke:#D1D5DB
style SYNTH fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
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
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% |
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.
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.
1. Universal Screening — All locations screened against revenue trends, cost ratios, conversion rates, and customer metrics.
2. Threshold Flagging — Locations flagged where costs outpace revenue, conversion declined >15%, or segments shifted >20%.
3. Multi-Dimensional Diagnostic — 24-month analysis across traffic, segmentation, category performance, and operations.
4. Pattern Recognition — Inflection points, leading indicators, and peer comparisons identified.
5. Insight Synthesis — Actionable findings, open questions, and risk prioritization delivered as executive narratives.
Six stages — from raw data to executive reports. AI doing real work in a structure that's trustable, on trustable metrics.
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flowchart LR
A["📍 Your\nLocations"] --> B["SCREEN
Multiple lenses\nin parallel"]
B -->|"Flagged"| C["INVESTIGATE
Deep diagnosis
15+ probes + ML"]
B -->|"Passed"| D["SAFETY NET
Health checks
AI escalates if needed"]
C --> E["SYNTHESIZE
AI writes narratives
Root cause + actions"]
D --> E
E --> F["ROLL UP
Location → District
→ Region → Exec"]
F --> G["📊 REPORTS
Client-ready
at every level"]
style A fill:#F8F9FA,color:#130417,stroke:#D1D5DB
style B fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
style C fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
style D fill:#fff,color:#130417,stroke:#D1D5DB
style E fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
style F fill:#fff,color:#130417,stroke:#D1D5DB
style G fill:#FEF3F7,color:#130417,stroke:#E3165B,stroke-width:2px
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.
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, what they'd recommend.
We turn that into structured investigation contexts — screening lenses, diagnostic probes, pattern rules.
We run it on your real data. Your team reviews. "I didn't see that" is the best feedback we can get.
Once tuned, it runs autonomously — every location, every cycle. Your people review findings and act.
"We don't come in claiming to know your business. We come in knowing how to learn it — fast."
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.