01
Storefront search & answers
Search and Q&A grounded in your catalog — product data, metafields, policies — so the model can't invent a spec or a discount code.
ai & data
AI systems that are practical, traceable, and useful.
AI features for ecommerce operations, grounded in your catalog and your own data — built by the team that built the store. We've shipped two of our own in the Shop app, and we'll tell you when the honest answer is “not yet”.
decisioning canvasactive
focus areas
Built around operating decisions, not model demos.
02
Support triage
Classify, draft, and route inbound tickets — in Gorgias, Zendesk, Intercom, or whatever helpdesk you run — with a human handoff designed in from day one. Faster answers, not an unstaffed inbox.
03
Internal copilots
Assistants over your order, inventory, and customer data, for the questions your ops team currently answers by exporting CSVs.
04
Product-content enrichment
Descriptions, attributes, translations, and alt text generated from source data and reviewed before publish. Worth it at 10,000 SKUs; pointless at 40.
05
Evals & guardrails
Golden test sets, regression evals on every prompt change, and hard rules the model can't talk its way past. This is where AI features live or die.
06
Providers & plumbing
OpenAI, Anthropic, and Azure AI — chosen per task, behind a thin abstraction, so switching providers is a config change rather than a rewrite.
straight talk
Where AI pays off first — and where we'd tell you to wait.
worth doing now
High-volume, low-stakes text
Support drafts, product content, translations. Mistakes are cheap to catch and the volume is real.
Answers over your own data
Catalog Q&A and internal lookups. Grounding in your data keeps the model honest and the scope tight.
Triage and routing
Classification is boring, constant, and exactly what models are good at. Nobody misses this work.
worth waiting on
Customer-facing output without review
An unreviewed model speaking for your brand is a liability before it's a feature. Start with drafts a human approves.
Autonomous pricing or inventory moves
Model output can inform these decisions. Letting it act alone is how you spend a weekend refunding mispriced orders.
AI for the roadmap slide
If nobody can name the decision or task it improves, it isn't a project yet. We'll say that in the free consultation.
Shipped, not slideware: Glow Coach and Wishpad, our two Shop Minis, are live in the Shop app today. Small, scoped, useful — the way we think AI features should start.
ai search visibility — aeo / geo
Get cited when buyers ask ChatGPT instead of Google.
A growing share of product and vendor research now happens inside AI assistants — ChatGPT, Perplexity, Google's AI Overviews, Copilot. Whether they mention you depends on how machine-readable and well-evidenced your site is. That work has a name — answer engine optimization (AEO), sometimes generative engine optimization (GEO) — and we set it up as a fixed scope, usually alongside classic SEO.
AI visibility audit
How assistants currently describe your brand — what they get wrong, which competitors get cited for your queries, and which sources those answers are pulled from.
Structure & schema
Organization, FAQ, product, and breadcrumb schema, an llms.txt company file, a deliberate AI-crawler policy, and content restructured into the question-shaped format assistants quote.
Authority & monitoring
The third-party listings and signals assistants check, plus periodic re-testing of how you're cited. The platforms change monthly, so this isn't set-and-forget.
Proof you can inspect: webkraftz.com runs this exact stack — the schema on every page, the llms.txt file, and the crawler policy of this site are the deliverable, live.
delivery model
Structured delivery from problem framing to long-term operation.
01
align on decisions
Name the decision or task the AI improves and who uses the output every day. If we can't name it, we say so and stop.
02
stabilize the data
Normalize the entities the feature depends on — products, tickets, orders — and fill the instrumentation gaps first.
03
ship in thin slices
A narrow version to a small group, measured against the eval set, then widened. Never a platform on day one.
04
operate with guardrails
Drift monitoring, monthly output reviews, and a non-AI fallback path that stays in place.
engagement
Scoped small on purpose.
Free consultation
We look at your data and your idea and tell you whether it's ready. Sometimes the honest first project is fixing product data, not adding a model.
Fixed-quote projects
First versions are deliberately narrow, scoped and quoted up front — sized so the eval set can prove the feature works.
Operate from $850/month
Monitoring, eval reviews, prompt and provider updates on a monthly plan. No long-term contract.
faq
Fair questions about AI work.
OpenAI, Anthropic, and Azure AI, chosen per task — cost, latency, and quality differ by job. A thin abstraction keeps the switch cheap when the market moves.
// related
Where this usually connects.
next step