X

Search here...
AI for retail & e-commerce

Custom AI/ML solutions for retail & e-commerce industry

Custom AI and ML for retailers, marketplaces, and DTC brands. From dynamic pricing and demand forecasting to personalization and shrink detection — built for omnichannel reality.
Book a Retail AI Strategy Session
5–12%

Margin lift per transaction from AI dynamic pricing across thousands of SKUs.

10–30%

Average-order-value uplift from per-customer personalized recommendations.

15–30%

Excess inventory reduction while maintaining availability and cutting stockouts 60–75%.

Achieve immediate, organization-wide results

Six measurable outcomes across pricing, personalization, and store operations, deployed in months, not years.

Dynamic Pricing

Optimize prices across thousands of SKUs against demand, competition, and inventory position. 5–12% margin lift per transaction.

Personalized Recommendations

Per-customer product, content, and offer ranking. 10–30% AOV uplift with 9-month average payback.

Demand Forecasting

SKU / store / channel demand models. Cuts stockouts 60–75% and excess inventory 25–40%.

Shrink & Loss Prevention

Computer vision and POS analytics that flag external theft, sweethearting, and self-checkout abuse in real time.

Marketing & CLV Targeting

Predicted-CLV scoring, churn prevention, and uplift modeling so spend goes where it lifts margin.

Search & Conversational Commerce

Semantic search, vector embeddings, and chat LLMs that convert browsers into buyers.

Capabilities across the retail & e-commerce value chain

Merchandising & Pricing

  • Dynamic and competitive price optimization
  • Promo and markdown effectiveness modeling
  • Assortment planning and SKU rationalization
  • Vendor-cost negotiation analytics

Inventory & Supply Chain

  • SKU / store / channel demand forecasting
  • Inventory allocation and replenishment models
  • Vendor lead-time and OTIF prediction
  • Last-mile delivery routing and slot pricing

Customer Experience & Personalization

  • Real-time product, content, and offer personalization
  • Search relevance and semantic vector retrieval
  • Predicted-CLV, churn, and uplift modeling
  • Conversational commerce and chat LLMs

Stores & Loss Prevention

  • Computer-vision shrink and self-checkout monitoring
  • Queue, traffic, and dwell-time analytics
  • Planogram compliance and on-shelf availability
  • Store-labor scheduling under predicted footfall
From the playbook

How a $1.2B specialty retailer lifted gross margin 380 bps with dynamic pricing

A specialty home-goods retailer with 280 stores and a $400M e-commerce business was leaving margin on the table with weekly manual price reviews across 60,000 SKUs. We built a price-optimization engine using competitor scraping, demand elasticity per SKU, and inventory-position signals. Margins lifted 380 bps within 6 months, sell-through on slow-moving inventory improved 22%, and the manual pricing team redirected to higher-leverage merchandising work. The same elasticity models now drive promo planning and markdown cadence — adding $48M to annual gross profit at a fraction of the engineering spend.

See more case studies →

START TODAY

Speak with a retail AI expert

A 45-minute scoping call. We’ll come prepared with benchmarks for your retail category and a directional read on which models move the needle on margin, conversion, and shrink.

Ask us about

  • Dynamic pricing across thousands of SKUs
  • Personalized recommendations and conversational commerce
  • Demand forecasting at SKU / store / channel level
  • Computer-vision shrink and loss prevention
  • Predicted-CLV, churn, and uplift modeling for marketing
  • Search relevance and semantic vector retrieval

    Contact Us
    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    Frequently asked questions

    Yes. We've shipped price-optimization engines that read from Salsify, inRiver, Akeneo, SAP, Oracle NetSuite, and Microsoft Dynamics. Prices push back to your e-commerce platform (Shopify Plus, Salesforce's Agentforce Commerce, BigCommerce, Adobe Commerce) and store POS through standard APIs. You own the integration code.
    For new SKUs, we use attribute-based embeddings to inherit signal from similar products until in-market data accumulates. For new customers, session-level vectors and behavioral cohorts kick in within the first 3–5 page views. We never wait for clean post-purchase data before personalizing.
    No. We deliver models with full MLOps wrappers — feature stores, retraining schedules, drift monitoring, A/B testing infrastructure, and dashboards. Your merchandising, marketing, and inventory teams interact through familiar BI tools, not Jupyter notebooks. Hand-off documentation supports both DIY operation and managed-service continuation.
    Yes. The underlying ML platform (feature store, model registry, monitoring) is reusable across channels. Domain models differ — physical retail focuses on shrink and labor, e-commerce on personalization and pricing, marketplace on competitive intelligence — but most omnichannel retailers run one shared stack with multiple model families.

    Explore AI/ML solutions for retail & e-commerce

    Ready to talk retail AI?

    Start with a 45-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.

    Book Strategy Session
    See All Industries
    Discuss your Retail & E-commerce project Discuss your project