Can it include advertising and marketplace fees in margin?

Yes, and that is usually the entire point. Gross margin before ad spend, marketplace commission, payment fees, and returns tells you very little about which products actually make money.

How current is the data?

Store and order data can be near real-time. Ad platforms and marketplaces are rate-limited, so those sync on a schedule — and every panel shows its own last-updated time so nobody acts on stale figures unknowingly.

How is this different from a standard BI tool?

The dashboard layer is similar. The difference is the work underneath — unifying systems that do not agree with each other — plus a question interface for people who will never write a query, and commentary that explains movement rather than just plotting it.

How fresh is the data?

Configurable per source. Operational metrics usually run near real-time; anything pulled from a rate-limited third-party API syncs on a schedule. Every panel shows its own last-updated timestamp so nobody acts on stale numbers unknowingly.

AI Analytics Dashboards · E-commerce

AI Analytics Dashboards for E-commerce

Your true margin lives across six systems and one exhausted spreadsheet.

Revenue is in the store, ad spend in the platforms, fees in the marketplaces, costs in the supplier records, and returns somewhere else entirely. Contribution margin by product only exists after someone spends a morning assembling it. A unified dashboard makes it a live number.

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Data freshness: LiveData freshness
Source of truth: OneSource of truth
In plain language: AskIn plain language

Typical stack

Next.js
Postgres
dbt
OpenAI
Recharts

Use cases

What this handles for e-commerce brands

Unify store, marketplace, ad platform, and supplier data into one margin view
Track contribution margin by product, channel, and campaign after all real costs
Monitor stock cover and sell-through against live sales velocity
Ask ad-hoc questions in plain language without waiting on an analyst

True margin visible per product and channel, continuously rather than monthly.

Context

The e-commerce problem underneath it

E-commerce volume scales faster than the team handling it. Support tickets, returns, supplier updates, product content, and channel reconciliation all grow linearly with orders — and all of them are largely mechanical. AI systems absorb that growth without proportional hiring.

The same 'where is my order' question dominates support volume around the clock
Returns and exchanges involve several systems and a lot of copy-paste
Product descriptions and attributes are a bottleneck on every new range
Inventory and pricing live in different places across marketplaces and your store

What we build

Inside an AI analytics dashboard

We define the metrics before we build anything, because most dashboard projects fail on disagreement about what a number means rather than on engineering. Once definitions are agreed, we pull data continuously, model it once, and expose both a fixed executive view and an ad-hoc question interface.

Connectors pulling from CRM, billing, ads, support, product, and operational databases
A single semantic layer so a metric means the same thing everywhere it appears
Live executive dashboards with drill-down to the underlying records
Natural-language querying for ad-hoc questions without SQL
Anomaly detection that flags unusual movement rather than waiting to be asked
Scheduled digests into email or Slack with plain-language commentary on what changed

Deliverables

What ships with the engagement

Agreed metric definitions documented and version-controlled
Data pipeline with freshness monitoring and failure alerts
Dashboard covering the decisions you actually make weekly
Natural-language query interface over the modelled data
Scheduled digest configuration per team

FAQ