Generative AI in Retail

LLM pipeline that generates brand-consistent, SEO-optimised product descriptions from your structured product data (attributes, specifications, dimensions, material) and product images. Model selection: GPT-4o with vision for product categories where image analysis adds context (fashion, homewares, food, the model describes colour, texture, and style details it can see); GPT-4o text for attribute-heavy categories (electronics, hardware) where the specifications carry the content. Brand voice system prompt encoding your tone guidelines, banned words, reading level target, and example descriptions from your best-performing existing copy, so every generated description matches your voice without post-editing. Output structured per your template: SEO-optimised title tag (under 60 characters), H1 heading, feature bullet points (5–7 items with benefit-first framing), and long-form description paragraph (150–200 words). Shopify Product API or Magento REST API integration to write approved descriptions directly back to the catalogue without copy-paste workflow. Batch generation via the OpenAI Batch API: submitting 1,000 SKUs overnight at 50% lower token cost than synchronous generation. Quality gate: a lightweight scoring prompt evaluates each output for completeness (did it reference the key attributes?), brand voice match, and factual accuracy against the input attributes before publishing. Descriptions that fail the quality gate route to human review rather than auto-publishing. Content production that took 15–20 minutes per SKU manually completed in under 30 seconds per SKU at scale.

AI customer support system handling the high-volume, predictable retail support queue, the queries that account for 60-75% of ticket volume but require no judgment to answer correctly: order status, shipment tracking, return initiation, refund status, product FAQ, and account changes. Technical architecture: a retrieval-augmented LLM with RAG grounding over your product catalogue, support knowledge base, and policy documents, ensuring the AI answers from your actual policies rather than hallucinating return windows or shipping costs it doesn't know. Live order data integration: OMS API (Shopify Admin API, Magento Order Management, or custom) queried in real time so the AI answers "where is my order?" with the current tracking status and estimated delivery, not a generic placeholder. Return initiation flow: AI collects order number, item, and return reason, validates against your return eligibility rules, generates the return label or return instructions, and updates the OMS, completing the return without agent involvement. Zendesk, Freshdesk, or Intercom integration for handoff: when the AI cannot resolve a query confidently (complaint, complex situation, sentiment below a configurable threshold), it escalates to a human agent ticket with the full conversation context and the customer's order history pre-populated. Scope guardrails prevent the AI from answering questions outside its designated domain (it won't give style advice or make promises beyond stated policy). Support contact volume reduction of 40–60% on standard retail support queues benchmarked across deployments.

Personalised product recommendations and shopping experiences built on your customer behavioural data, purchase history, and catalogue, beyond the generic "customers also bought" widgets that every ecommerce platform provides. Recommendation engine architecture: collaborative filtering for "users like you bought" signals combined with content-based filtering from product embeddings (product descriptions and attributes embedded with text-embedding-3-small and stored in pgvector or Pinecone) for cold-start coverage on new customers who have no purchase history. Recommendation contexts: homepage "recommended for you" (driven by recent browse and purchase history), product detail page "complete the look" or "frequently bought together" (item-to-item collaborative filtering), post-purchase email "you might also need" (complementary category recommendations). LLM-powered natural language search: customer types "something to wear to a summer wedding under £150", the query is embedded and matched to product embeddings, re-ranked by price filter and stock availability, with an LLM generating a short explanation of why each result matches the query. Shopify Storefront API or Magento GraphQL integration for real-time inventory-aware recommendations that don't surface out-of-stock items. Diversity controls preventing the recommendation widget from showing only one brand or category, ensuring varied options that reflect the full catalogue. Personalised promotional email recommendation blocks generated per subscriber segment from Klaviyo or Braze segment data, improving email click-through rates by 20–35% compared to static category promotions.

Automated merchandising content production for email campaigns, promotional banners, social ad creative, and on-site landing pages, generated from your product data, pricing, campaign brief, and brand guidelines without the per-piece manual production cost. Generation pipeline: a campaign template system where the merchandising team inputs the campaign parameters (offer, featured products, target segment, promotional window) and the LLM generates headline variants, body copy variants, and call-to-action variants for each surface (email subject line, email body, social ad primary text, social ad headline, on-site banner text). A/B variant generation built into the pipeline: for each campaign, 3–5 subject line variants generated and deployed as a split test, with the winner selected by open rate after 4 hours and auto-promoted to remaining sends. Seasonal and trigger-based content automation: the system generates and schedules promotional content for sale events (Black Friday, seasonal clearances) from a campaign brief rather than requiring manual copy production per sale. Klaviyo or Braze API integration for direct email content publishing; Meta Marketing API for social ad creative delivery. Content brief-to-first-draft time reduced from 3–5 hours of copywriting to under 10 minutes of review and refinement, enabling content teams to run more campaigns at the same headcount or redeploy copywriting capacity to higher-value strategic work.

AI search that understands customer intent beyond exact keyword match, converting natural language queries like "comfortable work shoes for standing all day under £80" or "gift for a 5-year-old who likes dinosaurs" into relevant catalogue results that keyword search would miss entirely. Search architecture combining two retrieval methods for best results: BM25 lexical search for exact attribute matches (size, colour, brand, SKU) merged with semantic vector search (product embeddings via text-embedding-3-small stored in pgvector) using Reciprocal Rank Fusion to blend both result sets before re-ranking. Hybrid approach outperforms either method alone, lexical search handles specific product lookups, semantic search handles conceptual and descriptive queries. Synonym expansion handled by the LLM at query time: "navy" maps to blue tones, "toddler" maps to size 2T--4T, "formal" maps to the suit and dress categories. Zero-results handling: when no results meet the confidence threshold, the AI suggests the closest matching category or related products and logs the failed query as a demand signal for merchandising review. Search personalisation re-ranks results using the logged-in customer's category affinity and price range from purchase history. Algolia or Elasticsearch as the underlying search engine with the AI layer handling query understanding and result re-ranking, rather than replacing battle-tested search infrastructure. Add-to-cart rate from search typically improves 15–25% compared to keyword-only search.

AI analysis pipeline that converts your sales history, inventory levels, and market signals into actionable merchandising decisions, delivered as natural language summaries the buying team can act on rather than dashboards requiring interpretation. Demand forecasting component: a Prophet or LightGBM time-series model trained on your 2–3 year sales history by SKU and category, incorporating seasonality (weekly, annual, promotional calendar), weather data for weather-sensitive categories, and Google Trends signals for trend-sensitive categories. Output: 4–8 week forward demand forecast per SKU used to generate automated replenishment alerts before stockouts occur rather than after they affect conversion. Markdown timing recommendations: stock-turn analysis per SKU identifying items holding markdown risk (slow velocity, approaching end of season, age threshold) with a suggested markdown percentage calibrated to clear remaining inventory by the end of the promotional window without unnecessary margin sacrifice. Assortment gap identification: comparison of your category distribution against basket association patterns, identifying which complementary products your customers are buying from competitors because they're missing from your catalogue. Weekly merchandising brief generated as a natural language summary (by an LLM synthesising the quantitative outputs): top 5 replenishment needs, top 3 markdown candidates with rationale, and top 2 assortment gaps, formatted as a Monday morning briefing the buying team actually reads rather than another dashboard they need to log into.