Custom AI automations that remove manual work from your Shopify operations, designed, built, and monitored by a team that has been building these systems since before most agencies noticed AI existed.
AI Workflows is our service for identifying, designing, and implementing automation systems that reduce the manual operational load on your team. We use tools including n8n, Zapier, Make (Integromat), OpenAI, Claude, and custom-built integrations to create workflows that handle repeatable tasks automatically, with AI decision-making built into the logic where it adds value. Unlike generic automation consultants, we build every workflow with your Shopify store as the operational center, ensuring automations work with Shopify's API behavior correctly and reliably. We document everything we build so your team can understand, modify, and extend it over time.
Everything in this service
A structured audit of every repeatable task in your operations, quantified by time and error rate. We identify which tasks are the highest-value targets for automation and prioritize accordingly.
Each workflow is designed before it is built: trigger logic, conditional branches, error handling, human escalation points, and monitoring requirements documented in a workflow specification.
Workflows built in your preferred automation platform or the one best suited to your use case, with correct error handling, retry logic, and execution monitoring configured.
Where AI models (GPT-4, Claude, Gemini) are integrated into workflows, for content generation, classification, summarisation, or decision support, we engineer and test the prompts that drive them.
Every workflow tested against real data before going live. Post-launch monitoring dashboards set up to alert your team (or ours) when a workflow fails or produces unexpected output.
Every workflow we build is documented: what it does, why it was built, how it works technically, how to troubleshoot it, and how to modify it without breaking it.
The difference between a seven-figure Shopify brand that is drowning in operational work and one that scales efficiently is almost always systems. Manual processes do not get easier as you grow, they get harder. Every new product, every new market, every new team member adds complexity to a manual workflow. An automated workflow handles ten times the volume with the same reliability it handles one. We embed AI into operations not because it is fashionable, but because it genuinely removes work that should never require human attention, and frees your team for the thinking that actually drives growth.
A structured interview and observation process to document every manual, repeatable task in your operations. Quantified by time cost and error frequency.
We score every automation opportunity by time savings, error reduction, and implementation complexity, then present a prioritized roadmap for your approval.
Workflows built in a staging environment, tested against real data, refined based on test results, and approved before production deployment.
Production deployment with monitoring configured. Monthly reviews of workflow performance, error rates, and emerging automation opportunities as your operations evolve.
There's a wide gap between the AI workflow conversations happening on LinkedIn and the AI workflows we actually build for Shopify operations. The LinkedIn version is breathless and abstract. The real version is mechanical: a defined trigger, a defined output, a confidence threshold, an escalation path, and a monitoring dashboard. Done correctly, the result feels less like "AI" and more like "an extra team member who works at 3am and never asks for time off." Done incorrectly, it feels like a fragile demo that breaks the moment real data hits it.
This page is for store operators who want to understand what AI can and can't reliably do inside Shopify operations today, where the leverage actually lives, and what implementation risk looks like.
The single most common build we deploy. The pattern: supplier sends CSV or spreadsheet with raw product data (title, dimensions, materials, basic specs). A workflow parses the input, runs each row through a prompt that produces a brand-voiced product description, generates 5 to 8 SEO-aware bullet points, suggests primary and secondary collections, and writes the meta title and description. Output goes to a review queue, not directly to the Shopify catalog. A human approves and one-clicks into production.
For a store adding 50 new SKUs per month, this collapses a 25-hour copywriting task into roughly 2 hours of human review. The economics are dramatic, and the brand voice consistency is actually better than what humans deliver because the same prompt produces the same tone every time.
Inbound support tickets get routed by category automatically (order status, returns, product questions, complaints, B2B inquiries, other), and a draft response gets generated for each one using a prompt that references your brand voice document and your macro library. Agents review and send. Average handle time drops by 40 to 60% within 30 days. Quality stays high because the human is still the final gate.
This pairs well with our customer support service when run together, but the workflow itself is independent of who's operating support.
Every Shopify store has implicit rules about how orders should be handled: B2B orders flag for review, high-value orders get fraud-check priority, gift orders need special packaging, international orders need duties calculation, repeat customers get loyalty acknowledgment. A workflow reads each order at creation time, applies your rules using AI for the ambiguous cases (was this a gift based on the note text?), and tags it for downstream operations. Eliminates 100% of the manual tagging work and reduces order processing errors materially.
The reviews already on your product pages contain everything your marketing team needs to write better ads, better email subject lines, and better product page copy. A monthly workflow pulls all reviews from the last 30 days, runs them through a synthesis prompt, and outputs a brief with: most-mentioned positive attributes (use these in ads), most-mentioned objections (address these in copy), surprising use cases (consider new positioning), and product issues that suggest a quality or sizing problem (feed back to product team). Takes a 6-hour analyst task down to a 10-minute read.
Suppliers communicate in formats that vary wildly: some send updates in spreadsheets, some in body text emails, some in PDF attachments, some via shared portals. A workflow watches a designated inbox or folder, parses incoming supplier communications, extracts the structured data (PO numbers, ship dates, quantity changes, delays), and updates a centralized PO tracker. This eliminates the manual data entry that usually buries one ops person for 4 to 8 hours per week.
Equally important is being clear about the boundaries. A few categories where the 2026 tech is still not quite ready for hands-off automation in production operations.
Brand-critical creative work. AI can draft a product description that's 85% of the way there. It cannot reliably produce final ad creative copy, brand voice guideline documents, or campaign concepts. Human creative judgment still wins here, the AI just speeds up the iteration loop.
Complex multi-step customer conversations. AI can draft responses. It cannot reliably handle a back-and-forth where the customer is upset, the situation is ambiguous, and a wrong response damages the relationship. We always route emotional or complex tickets to humans.
Financial decisions. AI is good at pattern recognition, bad at judgment under uncertainty. Reorder decisions, supplier swap decisions, pricing decisions, refund authorization above a threshold, all should stay human. AI can prepare the brief that informs the decision. The decision itself stays with a person.
Anything where being wrong is irreversibly expensive. If the workflow can't be safely reviewed before action, it shouldn't run on autopilot. We always design with a human-in-the-loop checkpoint for irreversible operations.
The platform choice is more important than most operators realize. Some general guidance based on building hundreds of these:
If you're already on a platform and it's working, we use what you have. If you're choosing fresh, we recommend based on the actual scope.
The work follows a predictable arc. First two weeks: structured audit of your current operations, with quantified time costs per recurring task. We come out with a ranked list of workflow candidates by ROI. Next two weeks: detailed design of the top 3 to 5 workflows, including trigger logic, error handling, escalation paths, and monitoring requirements. Following 4 to 8 weeks: build and test, one workflow at a time, with you reviewing each before production deployment. Ongoing: monthly check-in to monitor performance, optimize prompts, and identify the next wave of opportunities.
For clients also running Shopify store management with us, the AI workflows engagement is tightly coupled with daily operations. For clients running their own ops, we hand over fully documented systems your team can maintain and extend.
AI workflow engagements price based on scope. A single targeted workflow (e.g., product description generation) is a fixed-price one-off project. An ongoing engagement covering audit, multiple workflows, and continuous optimization is a monthly retainer.
The ROI math is usually straightforward. If a workflow saves 10 hours per week of operator time, and operator time costs even modestly ($30 per hour fully loaded), that's $15,000 per year. Most workflows we deploy save materially more. The retainer typically pays for itself in the first 90 days, often the first 30.
Book a free 30-minute discovery call, walk us through the operational tasks that eat the most of your team's week, and we'll tell you which ones we'd target first and what the expected time savings look like.
Book a free call and walk us through your most time-consuming operational tasks. We will show you what can be automated and what the time savings would look like.