AI for ecommerce + AI for retail — AI ecommerce development that lifts conversion without rebuilding your stack.

A 50K–500K SKU catalog typically sits on 40–70% items with thin or missing descriptions, missing alt text, and inconsistent attribute taxonomy. Manual enrichment runs $0.40–$1.20 per SKU; merchandising teams cap out at 200–400 SKUs per week, and the backlog grows faster than headcount can shrink it.

A pipeline takes the product image plus the supplier feed plus your brand-voice guidelines and generates a structured attribute extraction, an 80–120 word SEO description, alt text, and a three-tier taxonomy assignment. Humans review on a sampling cadence rather than per item. Brand-voice RAG over your tone-of-voice doc keeps the generation inside your style guide; an image-attribute extractor reads the photo for colour, fit, material, and visible feature signals the supplier feed didn't capture.

Algolia, Coveo, and native platform search return string matches. A query like "running shoes for wide feet under $120" returns brand-name matches not intent matches; 32–48% of search sessions end in "no relevant results" on long-tail queries. The conversion math gets ugly because the buyer is on a comparison day, not a browse day.

A query-understanding layer translates buyer intent into structured Algolia or Coveo facets and reranks the result set with semantic similarity over the catalog. We don't replace your search engine — we make its inputs and outputs smarter. The reranker reads a buyer's session signal alongside the query so a returning customer who's been looking at trail-running shoes for two weeks doesn't get reset to a cold result set.

Klaviyo, Bloomreach, and Nosto recommendations work — but the merchandising team can't see WHY a customer got the "warm picks" row. When a campaign underperforms, the team can't debug; when legal asks why a particular customer sees a particular row, you can't answer. GDPR Article 22 makes the answer mandatory once enforcement bites.

An explanation layer on top of your existing recommender. It drafts "we showed this because" reasons for the merchandising team's review console and surfaces the same signal to customers on demand. Your recommender stays — Klaviyo, Nosto, Bloomreach, whichever — and the explanation engine reads the same signals the recommender used. The customer-facing transparency surface ships as a small Vercel AI SDK component you embed wherever you need the disclosure.

Cart abandonment runs 68–82% across mid-market ecommerce. Email and SMS recovery captures 4–8% of abandoned carts; the rest is left on the table because the recovery message is generic and the buyer's actual blocker — sizing, stock, returns clarity — never gets named.

An agent reads the abandonment context — which products, time on page, prior purchase history, support history — and drafts a personalized recovery message. Where the buyer opted in, an in-app checkout-assist chat can answer "is this in stock in red size 9?" or "what's the return window for international orders?" against your live Shopify Storefront API, BigCommerce, or commercetools systems. The agent tool-calls; it doesn't guess.

Returns processing burns 3–7 minutes per RMA — photo review, condition assessment, refund-vs-exchange decision, restock route. At 8–15% return rates, ops cost is 18–32% of the original sale margin on the returned cohort. That's where the DTC margin goes.

An agent reads the customer's return reason plus photos plus order context, classifies the condition (resellable, refurbish, dispose), routes to the right path, and drafts the customer response. Humans approve the exception cases; routine ones auto-process inside the policy guardrails your ops team set. The agent never refunds outside policy — it just removes the manual-trace step that ate the team's afternoon.

AI inference costs spike 5–20× on Black Friday, Prime Day, and flash-sale days. Without cost caps, a successful AI feature becomes the most expensive line item on the P&L for that week, and the CFO comes asking on Monday morning. We've watched it. It's not fun.

A model-routing layer that downshifts from Claude Sonnet to Claude Haiku to a fine-tuned smaller model based on traffic load and per-session cost budget. Quality stays in the eval-acceptable band; cost stays predictable. The cost telemetry is per-use-case so the CFO can read "search reranking cost us $X on Cyber Monday" instead of one undifferentiated AI line.

Demand planners run weekly forecasts; missed signals — TikTok virality, regional weather, competitor promo — create 2–6 week stockout windows. Excess inventory ties up 8–18% of working capital. Both ends of the error distribution hurt the P&L.

An agent reads sales velocity plus external signals (social trends, weather, licensed competitor-promo data) and surfaces "this SKU is about to spike — increase reorder by N units" recommendations for the planner. The planner approves; nothing auto-orders. The base forecast is classical ML; the agent's job is the signal-narrative layer that makes the planner's review faster.