Demand you cannot predict
Forecasts miss promotions, seasonality and new products, leading to stockouts and markdowns.
Industries
AI & Data for Retail & E-commerce
Orders, loyalty records and support interactions often describe customers differently, while planners forecast demand in separate spreadsheets. We connect those data sources to consent-aware customer profiles, forecasts and commerce experiences.
Talk about your sector
Overview
Retail margins are decided by forecasting, pricing, personalization and operations, and each of those is now a data and AI problem. Customers expect relevant products, fast delivery and support that knows them. Retailers expect systems that keep up during peak season.
We build the customer data platforms, forecasting and recommendation engines, search experiences and commerce platforms that retailers and marketplaces run on, from the storefront to the warehouse.
Data Science & Predictive Modeling: Forecasting, churn, risk and optimization models, delivered by embedded data science teams.
Analytics, BI & Customer Insights: Semantic layers, dashboards and customer analytics people actually use.
Machine Learning, Vision & MLOps: Forecasting, vision and edge models with the MLOps that keeps them reliable after launch.
For a representative delivery approach, read Personalization on a unified customer data platform.
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Challenges
What gets in the way
Personalization that feels generic
Recommendations ignore context, inventory and margin.
Fragmented customer data
Online, in-store, loyalty and support data never form one profile.
Search that loses sales
Customers cannot find products that are in stock.
Peak-season fragility
Platforms and integrations strain under traffic and order volume.
Support cost at scale
Order, return and delivery questions overwhelm teams.
Bring the use case. We will bring the engineering point of view.
Solutions
What we build
Demand forecasting and replenishment
Hierarchical forecasts and inventory optimization integrated with planning systems.
Explore this servicePersonalization and recommendations
Real-time recommendation engines that consider context, inventory and margin.
Explore this serviceCustomer data platforms
Unified profiles, segmentation and activation across channels.
Explore this serviceAI-powered product search
Semantic and multimodal search with merchandising controls.
Explore this serviceCommerce platform engineering
Storefronts, order management, integrations and performance for peak traffic.
Explore this serviceCustomer service automation
Assistants for order status, returns and product questions with handoff to agents.
Explore this serviceWhere it fits
Typical use cases
- Store and SKU-level demand forecasting
- Personalized homepages, emails and offers
- Visual and semantic product search
- Returns and delivery support automation
- Loyalty and lifecycle marketing analytics
- Marketplace seller and catalog tooling
FAQ
Questions about Retail & E-commerce
Yes. We work with major commerce platforms and custom stacks, adding AI and data capabilities through their APIs and event streams.
Controlled experiments with holdout groups measuring conversion, basket size and margin, not just click-through.
Yes. We model promotional effects, use attribute-based approaches for new items and quantify uncertainty so planners can act on it.
Our cloud and quality engineering teams run performance testing, capacity planning and resilience work ahead of peak periods.
Related services
Capabilities that apply here
Next step
Ready when you are.
Tell us what you are trying to build and we will come back with a point of view, not a pitch.