I connect the operational data across your business, encode the definitions your team trusts, and build an intelligence layer that continuously shows you what changed, what is likely driving it, and where to act.
The delivery threshold where behavior changes, the segments where the relationship persists, the operational step contributing most to the delay, and the evidence behind each conclusion.
Shopify can show you the order. Your 3PL can show you the pick. The carrier can show you the delivery. Support can show you the complaint. Your subscription platform can show you the cancellation. None of them can reliably explain how those events affected one another.
Looking up one delayed order is useful customer service. It is not operational intelligence. The engine analyzes the complete population continuously, so your team can find the threshold, segment, and operational cause that actually matter.
At what delivery duration does repeat purchase begin to fall?
Does the effect change by product, geography, carrier, or cohort?
Did the warehouse, the carrier, or a failed delivery create the delay?
Could acquisition source, promotion, seasonality, or product mix explain it?
Does the relationship persist after relevant groups and time windows are compared?
Which intervention is worth testing, who owns it, and how will the result be measured?
The interface is only the delivery mechanism. The product is the connected data, governed definitions, analytical workflows, AI explanations, monitoring, and action controls underneath it.
Orders, fulfillment, shipping, support, subscriptions, reviews, finance, marketing, and inventory.
Resolve timestamps, products, customers, currencies, statuses, and source-specific language.
Encode governed definitions for revenue, delivery time, response time, churn, refunds, and margin.
Continuously test changes, segments, thresholds, relationships, risks, and opportunities.
Turn validated results into a clear narrative, supporting evidence, and focused hypotheses.
Deliver briefs, alerts, governed answers, human review, and approved operational actions.
Reliable intelligence comes from combining deterministic business logic with AI where language, synthesis, and flexible investigation create leverage.
A dashboard can be part of the system. It is not the system. I design the delivery around how your executives and operators actually make decisions: briefs, alerts, governed analysis, and controlled workflows.
Important changes, likely drivers, impact, and next investigation.
A focused notification when a validated condition moves outside its operating range.
Cross-functional dependencies and risks without the dashboard scavenger hunt.
Human-reviewed or approved automation when a known condition requires a response.
The increase is concentrated in delivery-related contacts and appears more closely associated with higher order volume and carrier delays than with a broad decline in agent performance.
Two regions and one carrier account for a disproportionate share of the change. The relationship persists after first-time and returning customers are compared separately.
Review carrier performance in the affected regions, adjust temporary support coverage, and test whether current delivery messaging is setting accurate expectations.
The starting question should be narrow enough to act on and valuable enough to matter. Once the foundation exists, each new question becomes faster to answer because the entities, definitions, and controls are reusable.
I designed, built, and continue to operate the underlying data + AI platform for a fast-growing commerce business. The same platform supports reporting, fulfillment, retention, customer experience, forecasting, creative intelligence, and operational monitoring.
I do not turn a new model loose on consequential workflows. We first compare its recommendations with real decisions, review misses, tighten the evidence, and define the conditions where it is allowed to write. Every applied action is labeled, traceable, and reversible.
Observe recommendations and failure modes before enabling writes.
Every conclusion links back to the governed metrics and source records behind it.
High-impact actions remain reviewable until the business explicitly changes the gate.
Changes made with AI are clearly denoted for later inspection and audit.
Applied changes retain enough context to inspect, correct, and roll back safely.
Freshness, quality, workflow health, cost, and failures are monitored continuously.
We begin with the decisions your current systems cannot support, not a predetermined tool list. The first engagement produces a working intelligence capability. Ongoing support keeps it reliable and turns the same foundation toward the next valuable question.
The final fee depends on the question, source-system access, historical data, entity resolution, and how much foundation work is required. The scope and fixed fee are agreed before kickoff.
Define the first high-value question, connect the necessary evidence, build the governed model, and deploy the first intelligence workflow into the way your team operates.
Keep the engine healthy, improve the analyses, add source systems, and deploy new questions and action workflows as the operation changes.
Delivered through the Automation Partner engagement—the same ongoing build-and-run capacity, specialized around this engine.
The right first use case is valuable, feasible with the evidence available, and connected to an action someone can actually take.
Scope an Engine LaunchFind the questions materially limiting growth, margin, retention, efficiency, or customer experience.
Identify the systems, entities, events, history, and definitions required to answer them.
Test access, data quality, ownership, consistency, and the ability to act on the result.
Connect only what the first use cases need, then deploy a useful intelligence workflow.
Review results with the operators who know the business and improve the evidence before adding complexity.
If your question is more specific, send it directly.
You can use an LLM after the necessary information is securely connected, cleaned, modeled, and governed. The model is the explanation and interaction layer; it is not the data foundation or the metric definition.
A warehouse or lake may be part of the architecture. The offer is the operating capability built on top: governed commerce entities, definitions, analysis, AI explanations, monitoring, and action workflows.
Dashboards are good at predefined visibility. This engine focuses on cross-system relationships, continuous analysis, explanation, evidence, and routing the next investigation or action.
It can identify changes, associations, segments, and statistically credible relationships. Strong causal conclusions may still require a controlled test or additional analytical methods. The engine makes that distinction explicit.
Only where you explicitly approve it. New use cases start in monitor mode. High-impact actions can remain human-reviewed permanently, while low-risk, well-understood actions can graduate to controlled writes.
I build in infrastructure and repositories you control. Your data, definitions, source code, credentials, and operating history remain yours.
Engine Launches are $15K+. The exact fixed fee depends on the first question, required source systems, historical data, access, entity resolution, and how much foundation work is needed. Scope and price are agreed before kickoff. Optional Managed Intelligence is delivered through Automation Partner at $7.5K–$15K per month.
In 30 minutes, we can map the decision, the evidence it requires, and whether a Commerce Intelligence Engine is the right way to solve it.
