Product content, support triage, and email personalisation built for DTC and multi-brand retailers. Brand-voice guardrails, human-in-the-loop where it matters. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
You run a DTC or multi-brand ecommerce operation doing $2M to $30M. Product content is slow for new SKUs, support tickets spike every time you run a sale, and your email personalisation is generic despite rich customer data. You've tried Gorgias AI or Klaviyo AI and they covered the basics — but brand voice, custom integrations, and cross-system data are still a manual problem.
The pain today
- New SKUs sit unpublished for days waiting on product copy
- Support volume spikes during promotions and buries the team
- Email campaigns are segmented but the copy still reads the same to everyone
- Previous AI content attempts sounded generic — customers noticed
- Platform AI (Gorgias, Klaviyo, Shopify Magic) covers the obvious cases, not your edge cases
The outcome you get
- Product descriptions and SEO metadata drafted from structured data in minutes, not days
- Support tickets triaged and drafted in your brand tone before a human touches them
- Email variants that read differently per segment because the copy was generated per segment
- Brand voice encoded in prompts so output matches your best existing copy
- API costs tracked and compressed monthly so the automation stays profitable
Where AI automation pays off fastest in ecommerce
Three workflows deliver clear ROI for most ecommerce operators. Product content generation — an LLM drafts descriptions, bullet points, and SEO metadata from structured product data. Human reviews before publish. Cuts content time by 60 to 80 percent per SKU. Support triage with response drafting — the LLM reads every incoming ticket, classifies it, and drafts a first response in your brand voice. Human agent reviews and sends. Cuts average handle time by 30 to 50 percent. Email personalisation — LLM generates segment-specific copy variants for campaigns. Marketer reviews. Lifts open and click rates because the copy actually matches the segment.
Each of these is 2 to 4 weeks of build time. I pick the one with the highest volume and clearest data first, ship it, then move to the next.
Product descriptions and SEO metadata drafted from structured data in minutes, not days
Product content that converts — without sounding generated
Generic AI content reads as generic because the prompt was generic. The fix I use: brand-voice system prompts built from your actual guidelines, your top-performing existing copy, and an explicit list of patterns to avoid. Structured product data feeds every generation call — category, features, materials, compliance flags, target keywords. The LLM drafts; a human reviews and edits. Over the first month, I update the prompts based on what your team edits out. Output quality converges toward your best human-written copy, not toward median AI copy.
I built a structured-prompt library for Instill, my own AI product — 1,000+ skills saved across 45+ projects. That same architecture applies directly to an ecommerce brand-voice library: a shared set of prompts for hero copy, product descriptions, email subject lines, and support responses that your team iterates on without touching code.
2M+: Records processed.
Support triage and on-brand response drafting
Most ecommerce support tickets fall into five or six categories: shipping status, return requests, product questions, discount problems, complaints, and custom requests. The first three are highly repeatable. The LLM reads the ticket, identifies the category, pulls order context from Shopify, and drafts a response using your voice and return policy. The agent reviews, edits if needed, and sends.
For simple categories like order status, the draft often goes out with a single click. For anything involving complaints or custom asks, the LLM triages priority but the agent writes the response. The system improves as agents edit — rejected drafts become training signal for the next prompt version. Support cost comes down without cutting the human judgment that matters.
Order and inventory automation for multi-brand operators
Beyond content and support, multi-brand operators have a second layer of high-ROI automation: order routing, restock alerts, and cross-system data sync. When your order management system, 3PL, and storefront speak different languages, the LLM acts as a translation and routing layer. Incoming order data hits a workflow (n8n or a custom Node.js service), the system identifies the SKU, the fulfillment rule, and the warehouse, and routes it without a human touching a spreadsheet.
I connected Reevia's four operational systems into a single HubSpot pipeline — 2M+ records processed, source-to-destination sync under 50 seconds, delivered in 4 weeks. The same integration pattern applies to ecommerce operators who have inventory, orders, and customer data scattered across tools that don't talk.
Integration stack: Shopify, Gorgias, Klaviyo, and beyond
The automations I build run on top of your existing stack. For product content, the Shopify Admin API reads product data and writes approved copy back. For support, the system integrates with Shopify Inbox or Gorgias, whichever you use. For email, it connects to Klaviyo or Mailchimp. Shopify Magic can coexist — I target the gaps that platform AI doesn't cover, not the overlaps.
For operators on n8n or Make for other workflows, AI steps slot in cleanly. For operators without a workflow platform, I build a lightweight Node.js service that owns the automation logic. Either way, you get full code ownership under Work Made for Hire. I never hold your API keys.
When platform AI is enough
Gorgias AI handles basic support triage if your team already uses Gorgias and your ticket categories are straightforward. Klaviyo AI handles basic segmentation for Klaviyo users. Shopify Magic handles commodity product copy for standard catalog items. If your brand is early-stage, your support volume is low, and your catalog is small, these tools likely cover enough at minimal cost.
My target customer is the operator where volume, brand voice requirements, or cross-system data complexity has outgrown what platform tools handle. If platform AI covers your actual use case, staying with it is the right call. I'd rather tell you that up front than take a retainer for work a $49/month plugin already does.
Pricing and what the retainer covers
The AI automation retainer is $3,999/mo. That covers build, integration, prompt engineering, monitoring, iteration, and monthly cost optimisation. LLM API costs pass through at cost — for ecommerce with high content or support volume, API costs typically run $200 to $2,000/month depending on throughput. Caching repeated outputs, compressing prompts, and routing simpler tasks to lighter models are part of the monthly work.
There's a 14-day money-back guarantee and no lock-in — cancel anytime. You own all code under Work Made for Hire. I hold one customer at a time by design, so I'm not running ten of these in parallel on a template.
Recent proof
A comparable engagement, delivered and documented.
Four systems, one source of truth: HubSpot visibility for one of Brazil's largest vet networks
Built a custom integration layer for Reevia that connects four source systems into HubSpot for one of Brazil's largest veterinary companies. Over 2 million records processed with full normalization. Any lead from any system is inside HubSpot in under 50 seconds, standardized and ready to use.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
I build system prompts from your actual brand guidelines, your top-performing existing copy, and a list of patterns your team explicitly does not use. For brands with formal voice documentation, the prompt captures tone, cadence, and vocabulary precisely. For brands without, I extract voice from your best existing product pages and email campaigns, then iterate. Human review catches drift. I update prompts based on what your team edits out. After 6 to 8 weeks, the gap between AI output and your best human copy is usually hard to find.
The LLM can only use structured data I explicitly pass in — product specs, materials, compliance flags, category attributes. It cannot invent claims. Categorical constraints are encoded as system rules: no unsupported health benefit claims, no outcome promises in regulated categories. Human review runs before every publish. For brands in supplements, skincare, or medical devices, I set up a legal-flag trigger so anything touching a sensitive claim category gets a dedicated human review before going live.
Shopify Admin API handles both sides. Product data pulls from Shopify, the AI generates and a human approves, then approved content writes back to the product record. For support, the system integrates with Shopify Inbox or Gorgias. For email, it connects to Klaviyo or Mailchimp. Where Shopify Magic already covers a workflow, I leave it alone and build around the gaps. You never need to migrate your stack to work with me.
Most ecommerce operators land between $200 and $2,000/month in API costs, depending on catalog size and support volume. Product content typically runs $0.01 to $0.05 per product. Support drafts run $0.001 to $0.01 per ticket. Email variants run $0.05 to $0.50 per segment. Prompt compression, output caching, and model routing for simpler tasks are part of my monthly work. I track cost per outcome and shut down automations that stop paying back.
Yes. Both have APIs I can build against. For Gorgias, the integration reads incoming tickets and writes AI drafts into the agent queue. For Klaviyo, it builds on top of existing segments to generate copy variants per segment. These aren't replacements for what the platforms already do — they extend them where the native AI runs out of headroom. If the native AI already handles your top use cases, I'll say so before taking a retainer.
First automation typically ships within 2 to 4 weeks. For product content, early drafts go through human review from day one — you start seeing time savings in the first two weeks. For support triage, reduction in handle time is measurable by the end of week three once the draft acceptance rate stabilises. The second automation runs in parallel starting week four or five. Most operators see full monthly cost recovery within the first 60 to 90 days.