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NextGen Code

E-commerce & Retail

AI for e-commerce and retail, built by a Shopify Partner

AI for e-commerce pays off in the unglamorous middle of the business: product data, inventory buys, support tickets, returns, and the email and SMS flows that bring customers back. NextGen Code helps Shopify brands, multi-channel sellers on Amazon, Walmart, and TikTok Shop, and local retailers with an online store put AI where it moves margin, not just where it demos well.

KPIs we baseline and move

  • 01Conversion rate
  • 02Average order value
  • 03Contribution margin per order
  • 04Return rate
  • 05Inventory turnover
  • 06Support cost per order

The opportunity

Product content, search, forecasting, support, and retention flows, tied to your store's real margins.

We're a Shopify Partner, and we've built Shopify storefronts for Braxley Bands, Mirra Skincare, and Infinite Coolers, so we know where store data tends to break: variants without attributes, inventory counts that disagree across channels, and attribution nobody trusts. We fix the data first, because every AI feature built on top of it inherits its mistakes.

Every project starts with your numbers: conversion rate, average order value, contribution margin, return rate, and support cost per order. If an AI idea can't move one of them, it doesn't make the roadmap.

What gets in the way

The problems we solve for e-commerce businesses.

  • Catalog content doesn't scale

    Every new SKU needs a title, description, attributes, alt text, and a version for each marketplace, and many catalogs carry hundreds of products with thin or duplicated copy. Thin product data hurts site search, Google Shopping, and the AI assistants that now answer shopping questions.

  • Inventory buys tie up cash

    Purchase orders often rest on last year's sales and gut feel. Overbuy and cash sits on warehouse shelves; underbuy and your best seller goes out of stock during the promotion you paid to run.

  • Support tickets repeat themselves

    Much of the inbox is the same short list: where's my order, can I return this, does it come in another size, why didn't my code work. Those tickets spike after every launch and holiday, and seasonal hires take weeks to get up to speed.

  • Returns quietly eat margin

    Return reasons sit in free-text fields nobody reads, so the same fit problem or misleading photo keeps generating returns. Each one costs shipping both ways, handling time, and often a product you can't resell at full price.

  • Marketing numbers don't agree

    Meta, Google, Klaviyo, and Shopify each take credit for the same order. Without one view of contribution margin by channel, budget decisions go to whichever dashboard sounds most convincing.

  • Multi-channel work multiplies

    Selling on Shopify, Amazon, Walmart, and in a physical store means separate listings, inventory feeds, and reports, usually reconciled by hand in spreadsheets.

AI use cases

Where AI pays off in e-commerce.

  1. 01

    Product content drafted from your own specs

    Before: a merchandiser writes each description by hand, and launches wait on copy. After: AI drafts titles, descriptions, attributes, alt text, Google Merchant Center fields, and Amazon or Walmart versions from your spec sheets and brand voice guide, and a person approves each batch in a review queue. For skincare, supplements, and similar categories, that review also screens for anything that reads like a drug or health claim.

  2. 02

    Site search that understands how customers ask

    Shoppers type "gift for a dad who grills" or "black dress for a summer wedding," and keyword search returns nothing useful. Semantic search, which matches meaning rather than exact words, finds the right products, and recommendations built on clean attributes suggest the add-ons that belong with them. We measure search conversion and zero-result searches before and after.

  3. 03

    Product data that AI shopping assistants can cite

    ChatGPT, Gemini, Perplexity, and Google's AI results increasingly answer product questions directly. Complete attributes, structured data, honest comparison content, and real reviews give those systems accurate material to recommend you from. This is generative engine optimization applied to a catalog.

  4. 04

    Demand forecasts for reorder decisions

    A forecasting model blends sales history, seasonality, the promotion calendar, and supplier lead times to recommend reorder quantities by SKU and channel. Example: trimming a $40,000 order of a slow mover by 20% without risking a stockout puts $8,000 of cash back in the business before next season.

  5. 05

    A support agent for order status and returns

    An AI agent connected to Shopify, your 3PL, carrier tracking, and your returns app answers order-status, return-eligibility, and sizing questions in chat and email, and hands everything else to a person with the full context. Example: at 1,500 tickets a month, 60% of them about order status and 4 minutes each, an agent that resolves those returns 60 hours a month to your team.

  6. 06

    Return reasons turned into product fixes

    AI reads return reasons, exchange notes, and reviews, groups them into themes like "runs small" or "color doesn't match the photo," and ties each theme to SKUs and dollars. The fix might be a size chart, a new photo, or a supplier conversation, and the return rate tells you whether it worked.

  7. 07

    Weekly review insights across channels

    Reviews from your site, Amazon, and Google are summarized each week into what customers love, what they complain about, and what they ask before buying. Product, marketing, and support each get the slice they need, and every recurring complaint gets an owner.

  8. 08

    Ad creative tests and email and SMS flows

    AI produces variations of hooks, headlines, and product images for Meta and Google tests, and drafts segmented Klaviyo or Postscript flows for welcome, browse abandonment, cart abandonment, and replenishment. Winners are judged on contribution margin per order, not clicks.

Compliance, built in

General guidance, not legal advice. We work alongside your counsel and compliance team.

  • The FTC's rule on consumer reviews and testimonials, in effect since October 2024, bans fake reviews (including AI-generated ones), buying reviews, and suppressing negative ones. Use AI to analyze reviews and to request them from every customer, never to write them.
  • Marketing texts require documented opt-in consent under the federal TCPA, and Texas extended its telemarketing law to cover marketing texts in September 2025. Email flows must follow CAN-SPAM, including a working unsubscribe link.
  • Under the FTC's Mail, Internet, or Telephone Order Merchandise Rule, you need a reasonable basis for any shipping time you advertise and must tell customers about delays. Configure support agents so they can't promise dates your operation can't keep.
  • Personalization runs on personal data. State privacy laws, including the Texas Data Privacy and Security Act and California's CCPA, give consumers rights such as opting out of targeted advertising, with different thresholds and exemptions in each state.
  • AI-written copy for cosmetics, supplements, and similar products can drift into disease or drug claims, which FDA and FTC rules treat very differently from cosmetic claims. None of this is legal advice; have counsel review claims in regulated categories.

Next step

Let's find the AI wins in your e-commerce business.

A 30-minute call with a consultant who knows your industry's workflows. You'll leave with two or three concrete ideas, whether or not we work together.

FAQ

AI for e-commerce: FAQ

Still have a question? Ask us directly.

What's the best first AI project for a Shopify store?

Usually product content or support automation, whichever hurts more today. If your catalog has hundreds of thin descriptions, AI-drafted content with a human review queue feeds site search, Shopping listings, and product pages at once. If the inbox floods after every launch, an agent that handles order status and returns is measured directly in hours saved. We decide using your Shopify reports and help desk data, not a hunch, and we'll tell you if neither is the right first move.

Do Shopify's built-in AI features make outside help unnecessary?

For simple tasks they're often enough, and we use them where they fit. Shopify Magic and Sidekick are useful for drafting product copy and emails and for handling admin tasks inside Shopify. Outside help earns its cost on work that spans systems: forecasting with 3PL and supplier data, support agents that need carrier tracking and your return rules, reporting that reconciles Shopify with ad platforms, and custom apps built on the Shopify API.

Will AI-generated product descriptions hurt our SEO?

Not if they're accurate, specific, and reviewed by a person. Google's published guidance focuses on whether content is helpful rather than how it was produced, while its spam policies target mass-produced pages with little value, no matter who wrote them. The real risk with AI copy is sameness: 400 descriptions that sound alike and repeat the spec sheet. We feed the model real attributes, use cases, and customer language from reviews, and every batch gets a human edit.

Can an AI support agent handle returns and refunds?

It can run most of the conversation and should hand off the money decisions you want a person to make. A well-built agent looks up the order, applies your return window and condition rules, creates a label through your returns app, and explains next steps. Refunds above a set amount, damage claims, and frustrated customers go to your team with the conversation summarized. You set the rules, and the agent applies them the same way every time.

We also sell on Amazon and in a physical store. Can you work across all of it?

Yes, and multi-channel sellers often have the most to gain, because that's where data gets reconciled by hand. We connect Shopify, Amazon Seller Central, Walmart, your POS, and your 3PL into one view of inventory, sales, and margin by channel, then add forecasting and reporting on top. You get one dashboard instead of five logins and a weekly spreadsheet, and reorder decisions finally account for every channel at once.