Generative AI in retail that scales content and support without scaling headcount.

• 01

  Product description generation
  An LLM pipeline that generates brand-consistent, SEO-optimised product descriptions from your structured product data and images. A brand voice system prompt encodes your tone, banned words, and best-performing copy so outputs match without post-editing, batch generation handles 1,000 SKUs overnight, and a quality gate routes failures to human review. Work that took 15-20 minutes per SKU completes in under 30 seconds.
• 02

  AI customer support
  AI customer support that handles the predictable retail queue: order status, tracking, returns, refunds, and product FAQ, the queries behind 60-75% of ticket volume. RAG grounding over your catalog and policies stops the AI hallucinating return windows, and live order-management (OMS) integration answers "where is my order?" with real tracking data. Low-confidence queries escalate to a human agent with full context, and contact volume typically drops 40-60%.
• 03

  Personalized shopping experience
  Personalized product recommendations built on your customer behavior, purchase history, and catalog, using collaborative filtering and product embeddings, beyond the generic widgets every platform provides, with cold-start coverage across homepage, product page, and post-purchase email. Natural language search handles queries like "something to wear to a summer wedding under £150" with inventory-aware results, and personalized email blocks lift click-through over static promotions.
• 04

  Merchandising content automation
  Automated merchandising content for email campaigns, promotional banners, social ads, and landing pages, generated from your product data, campaign brief, and brand guidelines. The team inputs campaign parameters and the pipeline produces headline, body, and CTA variants per surface, with subject-line variants split-tested and the winner auto-promoted. Brief-to-first-draft time drops from 3-5 hours of copywriting to under 10 minutes of review.
• 05

  Search and discovery
  AI search that understands intent beyond keyword match, converting queries like "comfortable work shoes for standing all day under £80" into relevant results. Hybrid retrieval merges BM25 lexical and semantic vector search, layered over your existing Algolia or Elasticsearch infrastructure rather than replacing it. Zero-result queries get closest-match suggestions and feed merchandising as demand signals, and add-to-cart rate from search improves when intent-matched results surface.
• 06

  Inventory and merchandising intelligence
  An AI analysis pipeline that converts sales history, inventory levels, and market signals into merchandising decisions delivered as plain-language summaries, not dashboards. Time-series demand forecasting drives replenishment alerts before stockouts, markdown timing clears slow stock without needless margin sacrifice, and assortment gap analysis flags missing complementary products. The buying team gets a Monday-morning brief with what to do first.

1. Week 1
   01

   Use-case triage

   Not every retail workflow pays off with gen AI. We rank your candidates on volume, repeatability, and data readiness. Then we scope the one with the shortest path to measurable ROI. You leave week 1 with a written scope and a fixed price.

2. Weeks 2-3
   02

   Catalog and data readiness

   Gen AI is only as good as the data it reads. We audit your product catalog, attributes, and support knowledge base, then fix the gaps that cause bad output. We build the retrieval layer, RAG, that the model grounds on. This is where hallucinated return windows and wrong product facts get designed out.

3. Weeks 3-6
   03

   Model, guardrails, and eval

   We wire the LLM to your data with a brand-voice system prompt and RAG grounding. Then we build the guardrails: scope limits and fact-checking against your catalog. An eval set scores accuracy, tone, and hallucination rate on real examples before anything reaches a customer.

4. Weeks 6-10
   04

   Human-in-the-loop rollout

   The v1 ships behind a review gate. High-confidence output publishes or answers on its own. Low-confidence cases route to your team with full context. As the eval scores hold, we widen the automation envelope. Trust is earned on your data, not assumed.

5. Weeks 10-14+
   05

   Measure lift and expand

   We track the metric that justified the build against a baseline from day one: support deflection, content cost per SKU, or add-to-cart from search. Once the first workflow proves out, we expand into the next one on the same foundation. Every project includes 8 weeks of post-launch support.

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