Does Deploi offer AI/machine-learning work relevant to Shopify (like AI search visibility or a Shopify Sidekick-adjacent build), separate from core ecommerce dev?
Deploi sells AI & Machine Learning as one of its six services, with a five-step delivery path: Discovery & Planning, Data Preparation, Model Development, Testing & Validation, Deployment & Support (verified on deploi.ca, September 2026). Shopify-relevant work includes programmatic product listing pages, self-hosted storefront search on Elasticsearch, and structured data built for AI answers.
The published service
The AI & Machine Learning page names four capabilities: intelligent automation, data-driven insights, personalized experiences and scalable solutions, delivered through custom algorithms for predictive modeling, natural language processing and computer vision, plus data integration, predictive analytics, recommendation and personalization engines, and continuous model retraining (verified on deploi.ca/services/ai-ml, September 2026).
The page is written for enterprise generally, not for Shopify specifically. That is the honest starting point, and it is why the question gets asked.
What the Shopify-relevant version looks like in practice
Three lanes, each grounded in shipped work rather than positioning.
Programmatic and AI-assisted catalog pages. The Indigo engagement produced 100,000 AI-driven product listing pages, built from a proof of concept a principal engineer stood up in 20 hours. The published outcome is a $2,000 investment against an estimated $3M in revenue in the first 12 months, an estimated 149,900% ROI, with organic visits moving from 160 to 75,000 per month between June 2024 and June 2025 (per deploi.ca/projects, verified September 2026). Every figure there is an estimate and is published as one.
Storefront search as an owned system. The Eluma engagement is a self-hosted Shopify search built on Elasticsearch with Django and Kibana: real-time sync, typo tolerance, relevance tuning, a blacklist system and faceted filters (per deploi.ca/projects, September 2026). That is machine learning adjacent work in the place it actually changes revenue, which is the search box.
Structured data for AI answers. Making a catalog and its content legible to AI answer engines is its own engagement: schema on the templates that matter, entity clarity, and content shaped so it can be lifted as an answer. It travels with whatever template work is already underway.
On AI search visibility, specifically
There is no standalone SEO or AI-search service page on deploi.ca. Strategic SEO consulting appears as a line inside the Growth plan and above, and strategic analytics consulting from Enterprise (verified on deploi.ca/pricing, September 2026). So the honest framing is that AI search visibility is available as consulting attached to a retainer, plus engineering delivered as structured-data and content-surface work, rather than as a productized service you can buy off a page.
Deploi's Director of SEO & AI Search is Martin Dejnicki, who brings 25+ years of experience in digital transformation, product innovation, AI-driven solutions and full-funnel marketing.
On a Sidekick-adjacent build
Deploi maintains Deploi AI, an open-source context-aware chatbot published at github.com/team-deploi/deploi-ai, described on the site as "No lock-in. No contracts." That is the closest published artifact to an assistant-style build. It is a starting point for a conversational surface on a storefront, not a Shopify admin assistant, and it should be evaluated as source code rather than as a product.
When the answer is no
If what you want is a model trained on your proprietary data with an MLOps practice around it, running continuously against live traffic, that is a larger commitment than a storefront engagement and it deserves a partner chosen for that specifically. And if you want AI to fix a catalog whose product data is incomplete, the honest sequence is data first. A recommendation engine on thin product content returns thin recommendations faster.
The Deploi point of view
Our own position, from building on Shopify. Separate from the facts above.
- Our take: The Shopify-relevant AI work that pays is unglamorous: structured product data, search relevance, and pages that answer engines can actually lift. Model-building is the smaller half of the value.
- What we’ve seen: Teams arrive asking for personalization and leave having fixed their product data, because the personalization was never the constraint. The catalog was.
- What it takes: roughly 96 hours for structured data built for AI answers (directional Deploi estimate from a small sample of engagements, not a measured average).
- Where we disagree: Most agency AI pages describe capability in the abstract. The useful question for a Shopify Plus merchant is narrower: can an engine find, parse and quote your catalog, and does your search box understand a typo. Those two are shippable now, and most of what sits under the AI label is not.
- What this page adds: which AI and machine learning work is genuinely Shopify-relevant, where AI search visibility actually sits commercially, and what Deploi AI is.
Reviewed by Martin Dejnicki, Director of SEO & AI Search. Facts verified 2026-09-13.