Shoppers now expect personalization, conversational answers, and agents that can act on their behalf — while margins stay thin and demand stays volatile. We help retailers move past the proof-of-concept and run AI systems that hold up against real catalogues, real traffic, and real basket economics.
Most retailers already run a recommender, a forecast, and a chatbot. The gap in 2026 is making them work together on a live catalogue, against agentic shoppers and thin margins — where personalization is table stakes, generative content has to be brand-safe, and a wrong answer reaches a customer in seconds.
Many personalization stacks were built on collaborative filtering years before generative models arrived. They rank yesterday’s behaviour well, but can’t reason over context, intent, or cold-start catalogue items — and they don’t plug cleanly into a conversational front end.
Personalizing assortment, offers, and search for every shopper across web, app, and store means scoring millions of decisions per minute within a latency budget. Most teams can do it in a notebook; far fewer can do it in production without breaking the page or the margin.
Promotions, weather, social spikes, and fragile supply chains make demand jagged. Forecasts trained on calmer years over- and under-buy, and the cost lands as markdowns and stockouts — the two largest, most avoidable drains on retail margin.
Generating product copy, support replies, and conversational answers across thousands of SKUs is where retail AI scales — and where it fabricates a spec or contradicts a policy. Without grounding and guardrails, the output reaches a customer before anyone checks it.
$1.7T
Overstocking and stockouts together distort an estimated $1.7 trillion of retail inventory worldwide each year — roughly 6.5% of global sales. The retailers pulling that back are the ones whose forecasting, pricing, and personalization run as one operating system rather than four disconnected pilots.
We work across the retail stack — from the storefront a shopper sees to the buying and operations decisions behind it. Each of these maps to a Miniml expertise we build and run in production.
Product, content, and offer recommendations that read live context, not just last week’s clicks — built to handle cold-start catalogue items and run inside a real page-load budget.
Recommendation systems →Conversational shopping and on-site search grounded in your catalogue, sizing, stock, and policy — answers a customer can trust, with the guardrails to stop a fabricated spec reaching them.
Knowledge & retrieval →Forecasts that hold up through promotions, seasonality, and supply shocks — built into the replenishment workflow so the buying team acts on them, and the markdown and stockout bill comes down.
Predictive analytics →Agents that resolve order, returns, and post-purchase questions across a real workflow — acting on your systems with the right tools, scopes, and human-in-the-loop checkpoints.
AI agents →Image-based product discovery, shelf and planogram monitoring, and shrink reduction at checkout and on the floor — vision models tuned to your store reality, not a benchmark dataset.
Computer vision →Pricing, promotion, and markdown decisions that weigh demand, elasticity, and margin together — with the assumptions and trade-offs surfaced so a merchant stays accountable for the call.
Decision intelligence →Retail AI rarely fails on the model. It fails on the data underneath it, the brand risk in front of it, and a measurement story the business doesn’t believe. These are the things we resolve before scaling anything.
Personalization is only as good as the identity and behaviour data behind it. We help resolve the fragmented customer record across web, app, store, and loyalty before any model relies on it.
Search, recommendations, and generated copy inherit every gap in your product data. We treat catalogue completeness and consistency as part of the model, not a separate cleanup project.
Generative output that faces a customer needs grounding in your own catalogue and policy, plus guardrails that catch fabricated claims — with human review at the points where being wrong is expensive.
Most attribution over-credits the channels easiest to track. We build measurement on incrementality and holdouts, so a personalization or pricing change is judged on the margin it actually moved.
First-party data carries obligations. We design systems that honour consent and opt-out signals from the start — so the personalization that drives revenue stays inside the rules.
Retail AI has to hold up on the busiest trading day of the year. We engineer for the latency budget and the traffic spike, not the average Tuesday in a demo environment.
Usually not. Most retailers get more from upgrading what they have — adding context and intent signals, fixing cold-start on new SKUs, and connecting it to a conversational front end — than from a rebuild. We start by measuring where your current stack actually loses revenue, then change the smallest thing that moves it.
By grounding it in your real catalogue, stock, and policy rather than the model’s memory, and by adding guardrails that block unsupported claims before they reach a customer. Where a wrong answer is costly — sizing, ingredients, warranties — we keep a human checkpoint and an audit trail. The goal is an assistant that says “I don’t know” rather than one that guesses.
Make your catalogue, pricing, and inventory readable and reliable for machines as well as people. The retailers that win the agent era are the ones whose product data is clean, whose APIs are dependable, and whose offers are structured. We help you get that foundation right before agents start transacting on your behalf.
With incrementality, not last-click attribution. We run holdouts and controlled experiments so a personalization, pricing, or forecasting change is judged on the margin it genuinely moved — not on credit it borrowed from demand that would have converted anyway.
That is the bar we build to. Personalization and search run inside a strict latency budget, forecasts and pricing are stress-tested against promotional spikes, and the architecture is sized for your busiest day rather than an average one. A model that only works in a quiet demo environment is not production.
Yes. The code, the models, the data pipelines, and the integrations are yours. We design every engagement so your team can operate, extend, and retrain the system independently — enablement is part of the project, not a separate upsell.
A 30-minute conversation with a senior consultant. Bring a recommender stuck in pilot, a forecast nobody acts on, or a shopping assistant you don’t yet trust in front of customers. We’ll tell you what it would take to put it into production — and what it’s worth to your margin.
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