AI by industry
AI for retail and e-commerce
We build the AI behind product search, recommendations, catalog data, demand forecasts and customer service for online stores and retail chains, connected to your commerce platform and proven in controlled experiments before full rollout.
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Retail AI that works on your real catalog
- Shopify
- Next.js
- Python
- scikit-learn
- PyTorch
- Elasticsearch
- Qdrant
- Redis
- BigQuery
- Claude
Retail AI rarely fails because of the model. It fails on incomplete product attributes, patchy event tracking, stock data that lags behind the warehouse, or a response that arrives after the page has rendered. Krapton designs retail and e-commerce AI around those details, so search, recommendations and forecasts perform on your catalog and your traffic rather than on a clean demo dataset.
The work spans the storefront and the back office. Shoppers get search that understands how they describe products, recommendations that respect stock and margin, and answers about their orders drawn from live systems. Merchandisers and planners get forecasts by product and location, cleaner catalog data and an early warning when a bestseller is about to run short.
Every customer-facing feature launches to a share of traffic against your current experience, with conversion, revenue per visit, margin and page speed tracked together. Rollout follows the measured result in your store, and monitoring continues afterward, because catalogs, seasons and shopper habits keep changing.
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Retail AI use cases we build
Most stores already hold the data these need, and every one of them changes what a shopper sees or what a merchandiser does next.
Search that understands shoppers
Hybrid keyword and semantic search that copes with synonyms, misspellings and long descriptive queries, then ranks results by relevance, stock and margin so shoppers land on a product page instead of a dead end.
Recommendations that respect stock
Related products, complete-the-look sets, cart add-ons and replenishment reminders learned from browsing and order history, filtered by availability and business rules, and tested against a simple bestseller baseline.
Visual search and image tagging
Shoppers search with a photo, and product images are tagged automatically for color, pattern, style and other attributes that feed filters, search and recommendations across the whole catalog.
Catalog enrichment
Missing attributes filled in from supplier sheets and product images, sizes and materials normalized, and descriptions drafted in your brand voice, with every change reviewed before it reaches the storefront.
Demand forecasting and replenishment
Forecasts by product, store and week that account for seasonality, promotions, price changes and past stockouts, giving buyers and planners order suggestions they can accept, adjust or reject.
Order and returns assistant
A chat or email assistant that answers questions about delivery status, returns and sizing from your order system and policies, and passes anything unusual to your team with the conversation attached.
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What a retail AI engagement delivers
Data and tracking audit
A review of catalog attributes, storefront events and order history, listing the gaps that would hold back search, recommendations or forecasts and how to close each one.
Relevance and forecast test sets
Real search queries with judged results, and held-out sales periods, used to score every change before shoppers or planners see it.
Storefront integration
Components for your Shopify theme, headless Next.js storefront or app, with caching, a latency budget and a sensible fallback whenever a model has nothing useful to show.
Experiment design and readout
The primary metric, guardrail metrics and test length agreed before launch, followed by a written summary of what changed and whether to roll out.
Merchandising dashboard
Searches with no results, recommendation engagement, forecast error by category and items at risk of running out, in one view for the commercial team.
Runbook and handover
How to reindex, retrain, roll back and add new signals, written for your developers, with the code and models kept in your own accounts.
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How we build retail AI
Four stages, each ending with a result you can check against numbers from your own store.
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Choose the moment and the metric
We pick one moment in the journey, such as search, the product page or post-purchase service, and the number it should move. Current performance is recorded as the baseline before any build work starts.
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Fix the data first
Catalog attributes, event tracking and order data are checked and cleaned. Missing events are added early, because recommendation and forecasting models can only learn from behavior that was recorded consistently.
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Build and score offline
We build the smallest version that could win and score it against real queries and past orders. It is tuned until it clearly beats the current experience on the test set, or the idea is dropped.
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Test live, then scale
The feature goes live to part of your traffic against a holdout group. If it wins on the agreed metric without hurting margin or page speed, it rolls out, and monitoring continues from there.
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Customer data, pricing and trust
Personalization runs on behavioral and purchase data, so it has to follow the consent choices shoppers make under privacy laws such as GDPR and the California Consumer Privacy Act. We design features that still work for anonymous visitors through session signals, keep sensitive categories out of targeting, and agree data retention with you before launch.
Anything a customer relies on, such as price, stock, delivery dates and return terms, comes from your live systems rather than from model memory. Generated product copy is reviewed before publication. Pricing and promotion models run inside floors, ceilings and approval steps, and never vary prices by personal characteristics. We also estimate running costs per request and plan caching for peak trading days.
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Related services
Retail software
We build the omnichannel commerce, POS, inventory and store-ops stack that lets modern retailers compete with platforms — and win on margin.
Add AI search to website
Your site search returns weak results and users bounce when they can't find what they need.
Build customer support chatbot
Support is drowning in tier-1 questions a chatbot could handle and your team is burnt out.
Improve e-commerce SEO
Product and category pages aren't ranking and your competitors take the long-tail you should own.
Predictive analytics and machine learning
Forecasts, scoring and anomaly detection from your data.
Computer vision development
Inspection, recognition and capture from images and video.
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Frequently asked questions
Can you add AI search and recommendations to a Shopify store?
Yes. We build them as Shopify apps or theme extensions, or inside a headless storefront on Next.js, using product, order and customer data from the Shopify APIs plus storefront events. The same approach works with other commerce platforms that expose catalog and order data through an API.
How much data do product recommendations need?
Less than many teams expect, provided it is clean. A smaller store can start with attribute similarity, co-purchase patterns and business rules, then move to behavioral models as traffic grows. New products are recommended from their attributes and images until they collect enough interactions of their own.
How do you prove an AI feature increased sales?
With a controlled experiment. Part of your traffic sees the new feature and the rest sees the current experience. We agree the primary metric, such as conversion or revenue per visitor, and guardrails such as margin, returns and page speed in advance, and run the test through full weekly cycles.
Could an AI shopping assistant give customers the wrong price or stock?
Not when it is built so that facts come from your systems. The assistant looks up price, stock, order status and policy through defined tools at the moment of the question, and the model only phrases the answer. Questions it cannot answer from those sources go to a person.
Can AI write our product descriptions?
It can draft them from supplier data, attributes and your style guide, which removes most of the blank-page work. Each draft is checked against the product data so it cannot claim a feature the item lacks, and a person approves it before publication. The drafts also help keep descriptions unique for search engines.
Does personalization work for first-time visitors?
Yes, within limits. Without a login or history, the page can still react to the current session: the category being browsed, the items viewed, search terms and the campaign that brought the visitor. As the shopper engages, recommendations update, and returning customers who have consented get suggestions based on past purchases.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.