AI and custom software for ecommerce brands.

Thousands of product pages to write, a support inbox that spikes every promotion, returns and order issues handled one by one: the parts of ecommerce that scale with headcount are the parts AI now handles well.

01why now

Ecommerce margins are squeezed from both sides: acquisition costs keep rising and customers expect faster service, better content, and more personalization than ever. Most of the operational work behind that, writing and updating product content, answering where-is-my-order, processing returns, and keeping data consistent across channels, grows in step with order volume.

AI breaks that link. Language models can write and maintain catalog content, resolve many routine support questions with access to order data, and handle returns within policy. Brands that do this well grow revenue without growing operations headcount at the same rate.

02what we build

• Catalog and content automation. Product descriptions, attributes, and SEO fields generated from supplier data and your brand voice, checked for accuracy, and kept consistent across your store and marketplaces.
• Customer service agents. AI agents that answer order status, shipping, sizing, and policy questions with real order data, take allowed actions like address changes or return labels, and hand everything else to a person with context.
• Order and returns operations. Exceptions, fraud flags, returns, and supplier communication handled through workflow automation with clear policy limits.
• Merchandising and reporting. Sales, inventory, and marketing data pulled into one view, with plain language summaries of what changed and why.
• Custom apps. Shopping, loyalty, and community apps built as native-grade mobile apps when a template app is not enough.

where to be careful

• Generated product content must be checked against real specifications. A confident wrong claim about a material or size costs returns and trust.
• Support agents need hard limits on refunds, discounts, and promises, with everything outside policy escalated.
• Personalization should run on data you have permission to use.

a typical first project

Support automation is usually the fastest payback. Pull the last few months of tickets, group them by reason, and look at how many are order status, shipping, sizing, or policy questions with a clear answer in your data. Those are the ones an agent can resolve, with a person reviewing a sample every week. Catalog content is a close second for brands with large or seasonal assortments, where new products wait days for descriptions and attributes before they can go live, and every day waiting is lost sales.

03who this is for

Direct to consumer brands and online retailers, typically past $10M in revenue, with a large or fast-changing catalog, a support team that scales with order volume, or an app ambition beyond what a template provides. It fits both brands on hosted commerce platforms and those with custom stacks.

Our public case studies are not ecommerce stores. The closest is consumer product work: Pinned Golf, where we turned a solo scorekeeping app into a social one and AI-augmented development reduced the engineering need from five engineers to one. The same approach keeps custom commerce software affordable to build and maintain.

04how it runs

1. Diagnose. Support volume and resolution time, content backlog, operations hours, and conversion data, each with a number.
2. Fix the budget. A scoped plan with a fixed number. Change requests are priced before they start.
3. Build in the open. Working software every two weeks, tested on real tickets, real products, and real orders before going live.
4. Operate. We stay past launch, especially through your first big promotion, to measure and harden.

The measures we track through launch are the ones your finance team already cares about: support cost per order, first response and resolution time, content backlog, and conversion on the pages we touch. If a change does not move one of them, it gets retired rather than maintained. Related services: custom AI development and custom software development.

05common questions about AI for ecommerce

How can ecommerce brands use AI?

Writing and maintaining product content at scale, answering routine support questions with real order data, handling returns and order exceptions within policy, summarizing sales and inventory data, and powering features inside custom shopping and loyalty apps.

Can an AI agent handle ecommerce customer service?

It can handle most routine questions, such as order status, shipping, sizing, and policy, and take allowed actions like address changes or return labels. Hard limits on refunds and discounts keep it safe, and everything else goes to a person with the context attached.

Is AI-generated product content accurate?

Only if it is checked. Content should be generated from real supplier and specification data and validated before publishing, because a confident wrong claim about a product costs returns and trust.

What should an ecommerce brand automate first?

Usually routine support questions, because they are high volume, easy to measure, and have clear answers in your order data. Catalog content is a close second for brands with large or seasonal assortments where new products wait for descriptions before going live.

Do you work with Shopify and other commerce platforms?

Yes. We build on top of hosted commerce platforms through their APIs, as well as on custom commerce stacks.

Do you build ecommerce mobile apps?

Yes. We build native-grade iOS and Android apps for shopping, loyalty, and community when a template app is not enough, and maintain them past launch.

How much does it cost?

A fixed number agreed before work starts. Change requests are priced before they are started, never after.