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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.
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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 underwriting, claims, and actuarial functions — 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 →

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Speak with a retail AI expert

A 45-minute scoping call. We’ll come prepared with your appetite, your loss-cost benchmarks, and a directional read on which models move the needle on your line of business.

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

    Can your pricing models work with our existing PIM and ERP?
    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 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 line of business and a scoped proposal.

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