AI Automation for Ecommerce Teams

AI Automation for Ecommerce Teams

Most AI pitches to ecommerce brands are a chatbot on the storefront. The systems that have actually earned their keep for me are the ones nobody outside the team ever sees: the call that gets scored before the agent has hung up, the ad that gets flagged before the budget is wasted, the report that answers a question the owner asked in a sentence.

I build those. This page is what that looks like, what it costs, and how to tell whether your store has one worth building.

One call to find the task

Scope a build

Bring the task that wastes the most time and the tool it lives in. Fixed price within a day of the call, or the name of the simpler thing to do instead.

Book a call

Want to see one running first

The write ups

Call scoring, the pre-call brief, the ads monitor and the weekly report, each with what it cost and what broke.

Read the case studies

What I have built, with the write ups

Six internal AI systems for an ecommerce team: call analysis scoring every recording, ad monitoring flagging fatigue from a sheet, a review pipeline turning praise into published proof, pre-call briefs for agents, MCP servers over the point of sale and ad accounts, and a fulfilment app that removed the manual label step

Call analysis. Every recording transcribed, translated where needed, scored for agent tone and customer sentiment, tagged by reason, written to a sheet the team already uses. Monday reporting went from two hours to ten minutes. The build, including what broke.

Ad account monitoring. A sheet that syncs from Meta on a schedule, with a model reading it to flag fatigue and new creative worth watching. Replaces a $200 to $500 a month dashboard. The build.

Pre-call briefing. An agent gets a five second summary of who is calling and why before they pick up. How it works.

Review pipeline. Praise that arrives in email and support threads, cleaned and published as reviews with permission. Why this one mattered most.

MCP servers. The owner asks the point of sale about Saturday’s sales in plain English. Google ships official servers for Analytics and Ads; when a system has none, I write one.

Fulfilment tooling. Not AI, but the same principle: an internal app that removed the manual label step and made shipping cost per order visible for the first time.

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Not sure which task to automate first? It is a scoring exercise, on one page. The builds that usually win are the weekly report and support triage with a monthly read. The cheapest first step of all is connecting the tools that already ship a server: Klaviyo, Google, and Shopify.

All six, and the principle that ties them together, are in AI workflows for Shopify and in the book of the same name.

How it is scoped

One call to find the task the team most resents. Then a fixed price for a defined outcome:

No retainer, no platform fee.

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Running cost after handover is usually $15 to $50 a month in model usage, because these are thin layers over tools you already pay for.
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About a third of the time the answer is a spreadsheet and a Zap. I would rather tell you that on the call than sell you a build.

Who this is for

It is for:

It is not for: a store that has not yet found its product, or a team that wants a chatbot to talk to customers. Both are fine goals. They are not what I build.

Start here

Bring the task that wastes the most time and the tool it lives in. If it is a fit, you will have a scoped price within a day of the call. If it is not, you will have the name of the simpler thing to do instead.

One call to find the task

Scope a build

Bring the task that wastes the most time and the tool it lives in. Fixed price within a day of the call, or the name of the simpler thing to do instead.

Book a call

Want to see one running first

The write ups

Call scoring, the pre-call brief, the ads monitor and the weekly report, each with what it cost and what broke.

Read the case studies

Frequently asked questions

What kind of AI automation do you build for ecommerce teams?
Internal systems, not customer facing chatbots. The ones that have paid for themselves: scoring every support call automatically, monitoring a Meta ad account and flagging fatigue, turning scattered customer praise into published reviews, briefing an agent before they dial, and MCP servers that let the owner ask the point of sale or the ad account a question in plain English. Each is written up on this site with what it cost and what broke.
Do I need a developer on my side?
No. Most of these run on tools the team already has, Google Sheets, Apps Script, a model API, and I build and hand over the whole thing. What I need from you is someone who knows the process being automated and can tell me when the output is wrong.
What does it cost?
Scoped per build after a call. The running cost of the systems themselves is usually $15 to $50 a month in model usage, because they are thin layers over tools you already pay for rather than new SaaS. The build is a fixed price for a defined outcome, not a retainer.
How long does a build take?
The first version of most of these ships in one to two weeks and runs on real data from day one. The call analysis pipeline took a weekend to a working version and a month to be trusted. Trust is the longer part and it is built by the team catching the model's mistakes early.
What is an MCP server and why would an ecommerce brand want one?
A small piece of software that lets an AI assistant such as Claude or ChatGPT read a system it otherwise could not: your point of sale, your warehouse, your ad accounts. Once it exists, anyone on the team can ask that system a question in plain English instead of waiting for a report. Google ships official ones for Analytics and Ads; for anything else I build them.
Is my data safe with this?
Everything I build is read only by default, runs under your own accounts and API keys, and sends the model only what the task needs. The write up of each system on this site covers where the data goes. If a workflow needs to write to a system, that is a separate decision we make explicitly.
Which should a store start with?
Whatever the team most resents doing. For most stores that is reading support tickets, reading ad reports, or answering the owner's questions about last week. Start with one, measure the hours it gives back for a month, then pick the next.