AI Consulting · E-commerce

AI Consulting for E-commerce

AI work that moves CAC, AOV, and conversion rate for DTC brands and Shopify sellers between $1M and $50M.

AI consulting for e-commerce

AI consulting for e-commerce is hands-on work for DTC brands, Shopify and Amazon sellers, and pure-play online retailers between $1M and $50M in revenue. It targets the metrics that actually matter (CAC, AOV, conversion rate, return rate, contribution margin) and skips the platform lock-in trap that comes from buying every AI feature your ESP or storefront vendor bundles in.

Use cases that pay off first

The AI plays we see deliver in e-commerce first, ordered by how fast they earn back the spend.

Product description generation that holds brand voice

An apparel DTC brand at $14M was launching 60 to 90 SKUs per drop. Copywriting was a bottleneck. The in-house copywriter spent two days per drop banging out PDP copy and never got to email or paid social. We trained a generation pipeline on 200 of her past approved descriptions, the brand voice doc, and a structured spec sheet from the product team. New SKU goes in, three description variants come out in her cadence, with the right hero benefits up top and the technical specs in the consistent format buyers expect. She edits, ships. The trap most brands fall into here is style collapse, where every product reads like it was written by the same generic narrator. We solved that by training on her work specifically, not generic e-comm copy.

PDP copy time per SKU dropped from 35 minutes to 6 minutes

Customer service that handles the boring 70 percent

A home goods brand at $22M revenue was burning 4 full-time CS reps on tickets that were almost entirely the same five questions: where's my order, can I exchange a size, did this ship to the right address, when's the restock, how do I cancel my subscription. We built a chat surface tied to Shopify order data, the 3PL's tracking feed, and the subscription tool, with hard handoff rules to a human the moment a refund, complaint, or sizing issue with a damaged product comes up. The bot resolves about 65 percent of inbound, fully, in under 90 seconds. Humans get the harder cases with full context already pulled. Two reps got reassigned to outbound retention work. Net Promoter Score went up because reps weren't burnt out anymore.

65 percent of CS tickets resolved without human touch

Abandoned cart emails that read like a person wrote them

A skincare brand at $8M was running the Klaviyo default abandoned cart flow. Generic. 14 percent recovery rate. We built a personalization layer on top that reads the actual products in the cart, pulls the buyer's history if they're a returning customer, and writes a 3-email sequence in the founder's voice that references the specific items, what's most often bought with them, and why someone hesitating on this stack typically pulls the trigger. The flow runs through Klaviyo (we didn't replace the ESP, we wrote into it). Recovery rate moved from 14 to 23 percent over a 60-day window. The founder's voice held up because we trained on 80 of her past emails first, not on a generic prompt.

Abandoned cart recovery moved from 14 to 23 percent

Common failure modes

The recurring ways AI projects stall in e-commerce. Worth flagging up front.

Brand voice collapse from generic AI copy

A footwear brand at $30M wanted faster PDP copy, so the marketing team plugged a generic AI tool into their Shopify backend and let it write descriptions from product specs. Within 90 days, every PDP read like the same midwestern explainer. Buyers noticed. Search rankings on the brand's higher-margin SKUs dropped because the descriptive language went generic and stopped matching the long-tail queries that drove organic. The fix isn't a prompt edit. It's training the system on the brand's actual approved copy first, then constraining the model to operate inside that style. Brand voice is a moat. AI without that moat is a leak.

Chatbots that anger customers when handoff fails

A pet supplies brand rolled out a chatbot that handled order status well. The trouble started when a customer's dog got sick on a new food and they tried to file a complaint. The bot kept routing them back to the FAQ. By the time a human picked up, the customer had screenshotted the loop, posted to Reddit, and demanded a full refund plus the shipping. The chargeback came in two weeks later. The fix is escalation rules baked into the architecture: any sentiment signal of frustration, any keyword set tied to product harm, any second turn of the same question, all hard-route to a human in under 30 seconds. Build escalation first, conversation second.

Vendor lock-in from bundled AI features

A beauty brand at $12M added Klaviyo's AI subject line generator, Shopify Magic for product descriptions, and the AI features inside their helpdesk tool. Twelve months later, half their copy lived inside three different vendor walls. When they tried to switch ESPs, the AI features didn't move. When they wanted to change PDP copy at scale, they had to rebuild the pipeline. The bundled-AI trap is real. Vendors price AI features as a feature wedge, not a service. Treat AI like infrastructure you own (your prompts, your training data, your API keys) and use vendor AI features only when the lock-in cost is genuinely zero.

Cost reality

What an AI engagement actually costs at each tier, and the failure mode that shows up when scope outruns budget.

Starter ($15K to $25K)

$15K-$25K

Includes:One workflow, one channel, fully built. Examples: PDP copy generation pipeline trained on your brand voice, abandoned cart personalization layer that reads into Klaviyo or Omnisend, customer service chatbot for the top 5 inbound questions tied to your Shopify order data. You get the working tool, API keys in your name, training data documented (so you can retrain or move it later), Loom walkthroughs, and a 30-day touch-up window. This is where most $1M to $5M DTC brands should start. One channel where AI moves a real metric, before scoping anything wider.

Failure mode:Trying to fit a multi-channel personalization platform into a starter budget. If you want PDP plus email plus chatbot plus paid ads, you're already at mid-tier. A watered-down starter version of all four ships nothing usable.

Mid ($25K to $75K)

$25K-$75K

Includes:Multi-channel work or a deeper integration with your stack. Examples: PDP plus email plus on-site search, all running off the same product catalog and brand voice training set. Customer service chatbot tied to Shopify, the 3PL's tracking, and your subscription platform with full handoff routing to your CS desk. Paid ads creative pipeline that produces variants for Meta and Google off a structured brief, with guardrails on spend. Retention email personalization plus winback flows. Includes a written measurement plan with the specific CAC, AOV, conversion, or recovery rate metrics we expect to move and how you'll verify.

Failure mode:Buying a build that depends on a vendor whose API you don't actually have access to. Some Shopify Plus features, some Amazon Seller Central data, and some loyalty platforms are gated behind plan tiers or partner programs. Confirm access before scope lock.

Strategic ($75K to $200K)

$75K-$200K

Includes:Catalog-scale infrastructure or multi-brand operations. Examples: a $30M+ brand with 5,000+ SKUs building a centralized product content engine that produces PDP copy, alt text, image tags, ad creative, and email content from one structured catalog. A multi-brand operator (3 to 8 brands) building shared infrastructure with per-brand voice tuning. A subscription brand building churn-prediction models tied to retention email triggers, customer success outreach, and product recommendations. Includes architectural documentation, a 12-month roadmap, and a written governance framework so your team can run it without me after handoff.

Failure mode:Treating this tier as a product launch instead of an infrastructure build. Strategic engagements ship in 90-day phases with a measurable metric move at the end of each. If month 4 has no usable thing in production, the engagement is failing in real time.

Our process

How an AI consulting engagement unfolds for e-commerce clients.

Discovery

Two structured calls and a metrics dump. What's your CAC, AOV, contribution margin, and return rate by channel. What's your stack (Shopify, Klaviyo, Gorgias, Triple Whale, etc.). What's the bottleneck right now: copy, ads, retention, CS volume, or something else. Output is a one-page brief naming the specific metric we'd target and a go or no-go recommendation. If your situation is wrong for me (pure marketplace arbitrage, dropshipping, anything gray-market), you hear that here.

Scope Lock

Fixed-fee proposal with explicit deliverables, the metric we expect to move, the systems we're touching, and the measurement plan you'll use to verify. Mutual NDA before any product data or customer info moves. Statement of Work before any code. No mid-engagement scope creep, change-orders for new asks. We agree on the data residency posture upfront, especially if you sell into the EU or California.

Design and Architecture

Architecture diagram, data-flow diagram, and a documented training set. For brand-voice work, this means we collect 80 to 200 of your approved past pieces (descriptions, emails, ad copy, whatever channel applies) and define the voice constraints in writing. For integration work, this means confirming the API surface from your platforms is real and stable. You sign off before we build.

Build

Iterative builds in 1-week sprints with a working demo at the end of each. For customer-facing surfaces, we run a structured red-team session before launch where I deliberately try to break the system, generic prompts, edge cases, frustrated customers, weird SKU data. Findings go in before any real buyer touches it. For internal tools, your team uses the build before launch and signs off on the workflow.

Handoff

Written documentation: what the system does, what data it uses, how to retrain it when your catalog or brand voice evolves, who has access to what. Loom walkthroughs for each user role on your team. API keys transferred into your company's name. The training data and prompts live in a repo you own. 30-day touch-up window included. After that, retainer if you want one, or run it yourself with everything documented.

Frequently asked questions

Does this work with Shopify, Amazon, or WooCommerce?
All three, with different tradeoffs. Shopify (especially Plus) has the cleanest API surface for product, order, and customer data, so most builds I do live there. Amazon Seller Central gives you SP-API for catalog and orders but the rate limits and token complexity make integration more expensive, and brand voice control on Amazon listings is constrained by Amazon's content rules. WooCommerce is fine for catalog and orders but you typically end up writing more glue code because the third-party plugin ecosystem is fragmented. If you're on BigCommerce or Salesforce Commerce Cloud, those are workable too, just confirm the specific API surface during discovery.
Will this lock me into Klaviyo or can I keep my options open?
I write into Klaviyo, not on top of it. The personalization logic, the prompts, the brand voice training, and the customer data flow live in infrastructure you own. Klaviyo is the delivery layer. If you switch to Omnisend, Sendlane, or back to Mailchimp, the AI work moves with you. I'll use Klaviyo's AI features when they're genuinely free and don't add lock-in (the AI predictive analytics, for example), and skip them when the price is portability.
How do you preserve brand voice when AI writes the copy?
Train on your work, not generic prompts. I collect 80 to 200 approved past pieces (PDP copy, emails, ad copy, social, whatever the channel) and feed that into the system as the voice baseline. Then I write specific constraints in plain English: this brand never uses adjectives X or Y, always opens product copy with a benefit not a feature, runs sentence length between A and B characters. The output gets reviewed by your copywriter or marketing lead for the first 60 days and they flag anything off-voice. Those flags go back into the system. After 60 days, brand voice drift is measurable in the low single digits.
Can AI write product descriptions for fashion or luxury brands?
Yes, but with caveats. For fashion mid-market and below ($30 to $300 price points), AI-assisted PDP copy works well when trained on the brand's voice. For luxury ($500+ price points), I'd build it differently: AI generates a structured first draft from product specs, your copywriter rewrites it from scratch with the AI draft as a checklist of points to hit. Luxury buyers can sense generic copy at 20 paces and the brand erosion isn't worth the time savings. Same logic for any brand where voice and craft are part of the price tag.
What about customer data privacy and CCPA or GDPR?
Customer PII gets minimized, not sent in full. Most workflows don't actually need email plus name plus shipping address plus order history; they need just enough to do the task. We architect around minimal data exposure: names get tokenized, emails get hashed where possible, full shipping addresses don't move into the LLM context unless the workflow specifically requires it. For EU customers, we use a data residency posture (Azure OpenAI EU or Bedrock EU regions) that keeps inference inside the GDPR-compliant zone. Your privacy policy gets a written addendum describing the AI processing so you're CCPA-ready.
Can a chatbot handle returns and refunds?
It can handle the lookup, the eligibility check, and the label issuance. It should not handle the judgment call when a return is borderline (worn item, past the window, damaged in transit and the customer is upset). Handoff rules: anything involving a refund decision over a threshold you set, anything where the customer expresses frustration, anything where the product was damaged or arrived wrong, all route to a human in under 30 seconds. The bot does the boring 70 percent. Humans handle the cases that need judgment. That ratio is where good CS economics live.
Is automating ad spend safe? I've seen brands burn money on this.
Burn risk is real and the cause is almost always the same: an automation that adjusts spend without hard guardrails. Daily spend caps. Per-campaign caps. Hard floors on ROAS or CAC. Required human approval before any budget shift over a threshold. We architect around the guardrails first, the optimization second. The right place for AI in paid ads is creative variant generation (which costs you copywriter time, not media spend) and reporting summaries that flag underperforming campaigns for your media buyer to act on. Full-autopilot spend reallocation is where brands get hurt.
Can AI translate my store for international markets?
Yes, but localization is more than translation. A literal Spanish translation of US PDP copy reads as machine output to Mexican or Spanish buyers because cultural references, units, payment expectations, and shipping norms don't translate. Better workflow: AI generates a base translation, then a native-speaker reviewer adapts cultural and product references. For brands selling into 4+ markets, the build pays back quickly. For brands testing one new market, hire a translator and skip the AI layer until volume justifies the build.
Can AI generate product images for my catalog?
For lifestyle and editorial imagery, yes, and the quality crossed the usability bar in 2024. For accurate product representation (the hero shot on the PDP, the variants on the size selector), no, you still need real product photography. Buyers can spot AI-generated product hero shots and the trust hit is meaningful, especially in apparel and beauty. The right split: real photos for the hero and variants, AI-assisted compositing for lifestyle backgrounds, AI generation for blog and social content where the image isn't the product.
How do I measure if this is actually working?
Pick one metric per workflow before we start. PDP copy build, conversion rate on the affected SKUs over a 60-day window. Email personalization, recovery rate or revenue per email. Chatbot, percentage of tickets resolved without human handoff and CSAT on the resolved ones. Ad creative, ROAS on the AI-variant campaigns versus the control. The measurement plan goes in the SOW so we're not arguing about success criteria after the fact. If the metric doesn't move, that's data, and we either iterate or kill the build.

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