Built by our app and integration engineers
AI commerce solutions for Shopify stores
OUXP builds AI features for Shopify and Shopify Plus brands whose team spends hours every week on work that needs reading and judgment: support emails, supplier spreadsheets and PDFs, product copy and tagging, and shoppers who can’t find the right product. We check Shopify’s built-in tools and simple rules first, then pilot one workflow against how long the task takes today – with a person reviewing what the AI produces.
Where AI pays off in a Shopify store
Many businesses know AI matters but struggle to see where it creates real value. In a Shopify store, it is usually in these six areas. In each one the AI takes the repetitive reading, sorting and drafting. Refunds, price changes and what gets published stay with your team – and where that line sits decides whether a workflow is safe to run.
-
Search and product discovery
- What you see
- Shoppers search in their own words, get no results or the wrong products, and leave.
- What the AI does
- Finds what shoppers mean – a search for “rain jacket” also returns products described as “waterproof shell” – and suggests related and complementary products, personalized from each shopper’s browsing and purchase history. As an on-site shopping assistant, it answers product questions from your catalog. Searches that return nothing are reported, so the gaps get fixed.
- What stays with your team
- Boosts, pinned results and synonyms. Ranking changes are tested against the current search before they replace it.
-
Customer support
- What you see
- Your support team is overwhelmed by the same questions every day, and response times slip.
- What the AI does
- Answers order-status, shipping and returns questions in live chat and email from your order data and approved policies, and drafts replies for your agents.
- What stays with your team
- Refunds, complaints and anything unclear go to a person with the conversation attached. The AI answers only from sources you approve.
-
Merchandising
- What you see
- Managing thousands of products, collections and merchandising decisions takes your team too long.
- What the AI does
- Proposes tags, categories and collection placement, and suggests cross-sells from what customers actually buy together.
- What stays with your team
- Your rules set what the AI may touch – for example, never move pinned bestsellers. A merchandiser signs off before any collection changes.
-
Catalog and product data
- What you see
- Your team spends hours updating products by hand – retyping supplier spreadsheets and PDFs, chasing missing details and writing copy.
- What the AI does
- Turns supplier spreadsheets and PDFs into structured fields and metafields, flags gaps, and drafts titles, descriptions and SEO metadata.
- What stays with your team
- The workflow leaves missing data empty and flags it instead of filling the gap. A person approves each record before it is published.
-
Marketing content
- What you see
- Product descriptions, landing pages, SEO content and email campaigns take significant time to create.
- What the AI does
- Drafts collection copy, landing-page sections, blog outlines and email variations in your brand voice, built from your product data.
- What stays with your team
- An editor checks every claim before publishing. Product facts come from the catalog, not from the model’s memory.
-
Internal tools and reporting
- What you see
- The store generates plenty of data, but turning it into decisions is slow – and staff hunt through documents for answers.
- What the AI does
- Answers staff questions from your SOPs, specs and policies, compiles agreed figures into reports and flags exceptions, and reads supplier purchase orders into your systems.
- What stays with your team
- Assistants look things up – they don’t act on their own. A named person decides what to do about a flagged exception.
Also in scope
- Sales and inventory forecasts built from your own order history
- Customer segments from purchase behavior, for email and ads
- Personalized email content and product picks based on each customer’s browsing and purchase history
- AI connected to your ERP, CRM, PIM or OMS through commerce integrations
- Custom Shopify apps that put AI tools inside your admin
- Dashboards that pull Shopify and ERP figures into one view
- AI-assisted analysis of session and funnel data for conversion rate optimization
- AI search and recommendations built into a custom storefront through headless commerce
- Chat and recommendation widgets checked for their load-time cost – with performance optimization if they slow your pages
AI use cases by team
| Team | Typical use cases |
|---|---|
| Customer experience | An on-site shopping assistant, personalized product discovery, order tracking and returns support |
| Marketing | Content drafts, SEO metadata, personalized emails and campaign planning |
| Operations | Workflow automation, inventory forecasting, product data management, internal knowledge search and an operations copilot that answers process questions from your SOPs |
| Leadership | AI-compiled reports, business dashboards, sales forecasts and operational insights |
| Sales and support staff | Sales, support and product information assistants that look up products, orders and policies while staff talk to customers |
Is AI the right tool for the job?
Often it isn’t – and with AI tools changing this quickly, it’s hard to know which are worth paying for. Two checks come first: whether your store is ready for AI, and whether the task needs it at all.
Ready for AI if…
- A task repeats every day and eats hours of someone’s week
- The information it needs is already in your systems – catalog, policies, order history
- Someone can review the output during the pilot and own the exceptions
- You want proof from a pilot before committing to more
Not ready yet if…
- There is no specific task yet – only a sense that you should “do AI”
- The data the AI would need is missing or lives in people’s heads – we would fix the data first
- Nobody can review outputs in the first weeks
- The goal is to take people out of decisions that carry legal or financial risk
Then choose the simplest approach that works
| Approach | Example on Shopify | Fits when | Keep in mind |
|---|---|---|---|
| 1. Shopify’s built-in features | Sidekick drafts product descriptions and edits products in the admin; Shopify Inbox suggests chat replies; the Search & Discovery app adds synonyms, filters, boosts and recommendations | The feature already exists in your plan or a free Shopify app | No build cost – you accept Shopify’s defaults and limits |
| 2. A rules-based workflow | Shopify Flow tags high-risk orders and alerts the team that checks them | The steps are fixed and the conditions are clear | Predictable and cheap to run; built through app and automation development |
| 3. An integration | Stock levels from the ERP update Shopify; orders flow to the 3PL | Data has to move between systems on a schedule or an event | Needs monitoring and safe retries – see commerce integrations |
| 4. AI | Supplier PDFs turned into product fields; customer questions answered from your policies | The task needs interpretation: free text, images, messy supplier data, open questions | Always with a review step, plus usage-based model fees and ongoing monitoring |
Shopify features as of October 2026. Shopify’s own semantic storefront search – results that match meaning, not only keywords – needs the Grow, Advanced or Plus plan and a catalog under 200,000 products, so check it before paying for custom AI search.
Honest note
If a rule can do the job, we build the rule and call it automation, not AI. A rule costs less to run, is easier to test and behaves the same way every time. Many workflows end up as a mix: rules for the predictable cases, AI only for the ones that need judgment.
How do you keep AI from making mistakes on a live store?
No model is perfect, so the workflow around it has to catch errors. These boundaries are settled in writing for every workflow before we build it.
-
The AI makes something up
How we handle it
It works only from sources you approve – your catalog, policies and order data. When information is missing, the workflow stops and asks, leaves the field empty or uses an agreed default. Because a model can still get things wrong, a person reviews every output during the pilot – and after it, you set how much review continues based on the measured correction rate.
-
Changes reach the store or customers unchecked
How we handle it
We agree which drafts, bulk edits, price or stock changes and customer messages need a person’s sign-off before they go live. A named role owns every case the rules don’t cover, so exceptions never sit in a queue nobody watches.
-
Customer data goes where it shouldn’t
How we handle it
The workflow reads only the systems and fields it needs – many need no customer details at all – and its API access is limited to those. Before any store data reaches an AI provider, we check its terms – how long inputs are kept and whether they may be used for training – and review privacy and compliance for each tool and data set.
-
Nobody can tell what changed – or undo it
How we handle it
Each change records what changed, when, why and from which source, with an agreed way to reverse it. If a bulk edit goes wrong, it is rolled back from the log, not rebuilt by hand.
-
Running costs eat the savings
How we handle it
Before the build you see what remains after launch: model usage fees, app or service fees, monitoring, updates when your store or source data changes, and time spent on exceptions. If those outweigh the hours saved, the pilot shows it – and we stop.
How an AI project runs: pilot first, then scale
Six steps from a list of candidate tasks to a workflow your team trusts. The pilot is small on purpose – it shows whether the benefit survives review, exceptions and maintenance.
- 1
Opportunity assessment
We list the tasks that take your team’s hours and map each one – inputs, decisions, exceptions and owner – then rank them by time saved and risk.
- 2
Baseline
We measure how the chosen task runs today. Without a baseline, any savings figure is a guess.
- 3
Design and build
The simplest approach that works, boundaries in writing, built on a development store or staging – never on your live store.
- 4
Pilot
Limited scope, a set period, and a person reviews every output.
- 5
Review
What was corrected, missed or handed to a person – compared with the baseline.
- 6
Expand, adjust or stop
Roll out what worked to more products, channels or tasks, fix what the review showed – or stop if the savings don’t hold. Monitoring and new workflows can run on a monthly retainer, or with a dedicated engineer for a continuous AI program.
What gets measured
What AI work is meant to deliver: lower operating costs, less repetitive work, a more productive team, faster merchandising, better inventory planning, more satisfied customers and better-informed decisions – and room to grow without adding headcount. These measures show which of those your pilot actually delivers, before you scale:
| Measure | What it tells you |
|---|---|
| Handling time | Minutes per item from arrival to finished – review included, so the saving is real |
| Manual touches | Edits or hand-offs per item, before and after |
| Correction rate | The share of outputs changed at review – the clearest sign of how far the AI can be trusted |
| Unresolved exceptions | Open cases per week that still need a person |
| Operating cost | Time and fees per month to run and maintain the workflow |
| Shopper results | For search, recommendations or support: conversion, searches with no results, or hand-off rate, compared with what runs today |
Storefront features such as search and recommendations can be A/B tested against the current version, the same way our conversion rate optimization (CRO) work tests any storefront change.
What we build AI features with
The model is the smallest part of an AI feature. Most of the engineering sits around it: the data it reads, the approvals it waits for and the systems it writes to.
| Layer | What we use |
|---|---|
| AI models | Models from OpenAI, Anthropic (Claude) and Google (Gemini), through their APIs – chosen per task, with no lock-in to one provider |
| Knowledge retrieval | Search over your approved documents – catalog, policies, SOPs – so answers come from your sources, not the model’s memory |
| Agents and workflows | Multi-step workflows that call your systems, with approval steps wherever a person must sign off |
| Commerce AI | Shopify Search & Discovery, apps you already use or a custom build – for product search, recommendations, merchandising, personalization and customer support |
| Shopify | Admin and Storefront APIs (GraphQL), webhooks, Shopify Flow, Search & Discovery and custom apps |
| Your other systems | ERP, CRM, PIM, OMS, WMS, marketing platforms and internal APIs |
How OUXP uses AI in its own engineering
We use AI tools daily across all our services – under the same rules this page sets for your store’s workflows.
Where AI tools help us
- Mapping data fields in migrations
- Writing and running automated tests
- A first pass of code review and QA
- Reading errors and logs when troubleshooting
What stays with people
- Agreeing the brief and the acceptance checks before any tool is used
- A senior engineer reviewing every AI-assisted output before it ships
- Testing against the brief before anything reaches your store
- Release only after your approval
- Judging time saved by accepted work, not by drafts produced
What merchants say about our custom engineering
AI features are custom software on top of Shopify, built by the same engineers who build our apps, integrations and conversion tests. These reviews of that work are quoted word for word from the Shopify Partner Directory.
-
“Others told us it wasn't possible but Vitali created a central inventory system that works with our custom setup. Great work and communication.”
Dive Bar Shirt Clubvia Shopify Partner Directory (external site)Apps & integrations -
“The team delivered a strong, functional product… The quality of work, attention to details and execution was nothing less of tremendous… Thank you OUXP team!”
ZigZagZurichvia Shopify Partner Directory (external site)CRO
Reviews sometimes name our engineers – everyone named is part of the OUXP team.
Emergency support
Store down? Checkout broken?
- Store down or showing errors
- Checkout or payment failures
- A theme or app update broke something
- ERP, inventory or fulfillment sync stopped
Commerce engineers, not a help desk. Every request gets a first response within 30–60 minutes during business hours. Retainer clients with the 24/7 emergency add-on are covered around the clock. Not a client yet? Call anyway.
+1 (818) 651-3226 Emergency line 24/7 · Business hours Mon–Fri, 9 AM–9 PM ET
AI commerce questions
Something else? Ask an engineer – first response within 30–60 minutes during business hours (Mon–Fri, 9 AM–9 PM ET).
How can AI help a Shopify store?
Mostly by taking repetitive work that needs some judgment off your team: answering order-status and returns questions, turning supplier data into product fields and descriptions, tagging and sorting the catalog, and helping shoppers find products when they don’t know the exact words. It helps least with predictable tasks – a Shopify Flow rule or an integration usually does those better and for less. OUXP starts by mapping which of your tasks fall on which side.
Is AI worth it for a smaller store?
It depends on volume, not company size. If a task repeats every day – a steady flow of support emails, or new products arriving from suppliers every week – even a small team gets hours back every week. If it happens a few times a month, Shopify’s built-in tools, such as Sidekick in the admin or suggested replies in Shopify Inbox, are usually enough. We will say so rather than build something you then pay to maintain.
Do we need Shopify Plus for AI features?
No. Custom AI tools connect through Shopify’s Admin and Storefront APIs, which work on every paid plan, and Shopify’s own AI tools such as Sidekick come with the admin, though availability varies by plan and language. As of October 2026, Shopify’s semantic storefront search needs the Grow, Advanced or Plus plan. Plus matters when a feature has to change checkout: customizing the information, shipping and payment steps, or running Shopify Functions in a custom app, needs Plus – that work sits with our Shopify Plus development team.
Which AI models do you use?
We build with models from OpenAI, Anthropic (Claude) and Google (Gemini) through their APIs, and choose per task: how accurately the model handles your data in testing, cost per request, speed and the provider’s data terms. We aren’t tied to one provider, so the model can be changed later if a better or cheaper one fits.
Is our customer data safe when you build with AI?
A workflow only gets API access to the systems and fields it uses, and many need no customer details at all. Before any store data goes to an AI provider, we check the provider’s data terms, including retention and whether inputs may be used for training, and review privacy and compliance for each tool and data set. Every change is logged with a way to reverse it. Store access goes through Shopify’s collaborator permissions, never shared passwords.
How much does an AI project cost, and can you estimate the savings?
Each project is quoted after the AI opportunity assessment, because cost depends on the systems involved and how much review the output needs. Running costs – model usage fees, app fees, monitoring and time on exceptions – are estimated before the build. Savings are estimated only after measuring how the task runs today, and the pilot then shows whether they hold up. Ongoing improvements can run on a monthly retainer from $2,500/month.
Will AI change our store without anyone checking?
No. Which changes need a person’s approval – drafts, bulk edits, price or stock changes, customer messages – is agreed in writing before anything is built. During the pilot, a person reviews every output. After it, you decide how much review routine work still needs, based on the measured correction rate. Every change is logged with a way to undo it, and refunds, complaints and unclear cases go to a person.
Can you get our products into ChatGPT and other AI shopping assistants?
As of October 2026, Shopify lists eligible stores’ products in AI channels such as ChatGPT, Microsoft Copilot and Google’s AI Mode and Gemini through its agentic storefronts, managed in the admin under Sales channels > Agentic. Being eligible doesn’t guarantee your products appear – each assistant picks the products it thinks fit the shopper’s request. What OUXP can improve is what those assistants read: complete titles and attributes, structured metafields, accurate stock and clear policies – the same product-data work that makes on-site search better.
Which task would you stop doing by hand?
Describe it, how often it happens and who handles it today. An engineer replies within 30–60 minutes during business hours with a suggested first step – AI or not.