SKU-store demand forecasting
Machine learning forecasts demand per item and store using sales history, promotions, seasonality and local events, feeding replenishment suggestions that buyers and store managers review.
Predictive AnalyticsAI for Retail
AI in retail from stockroom to checkout.
We build AI in retail that helps merchants, store managers and associates keep the right products on the right shelves. From store-level demand forecasts to shelf monitoring and associate copilots, our systems connect to your POS, ERP and order management stack and respect shopper privacy at every step.
- Store-level forecasting
- Shelf availability insights
- Associate copilots
01 Overview
Retail AI that works on the shop floor
Physical and omnichannel retailers juggle thousands of SKUs, seasonal swings, promotions and fulfillment from stores, all while customers expect accurate stock information online. Practical AI in retail turns POS transactions, inventory feeds, planograms and loyalty data into decisions people can act on today: what to order, what to move to the front, which store should pick an online order, and where shelves are running empty.
We design for the realities of stores. Recommendations reach associates on handhelds they already carry, replenishment suggestions flow into existing ordering workflows for buyer approval, and camera-based shelf analytics focus on products and fixtures rather than identifying shoppers. Personalization uses consented loyalty data with clear opt-outs, and we avoid individualized pricing that would treat customers unfairly.
02 Challenges
What AI solves in retail.
The recurring problems we hear from retail teams — and where AI makes the biggest difference.
-
CH-01
Out-of-stocks and phantom inventory
System counts often disagree with what is physically on the shelf, causing missed sales, unfulfillable buy-online-pick-up-in-store orders and frustrated customers who trusted the website.
-
CH-02
Forecasting across thousands of stores
Local events, weather, store formats and promotions make demand vary sharply by location, so chain-wide averages leave some stores overstocked while others run short.
-
CH-03
Thin staffing on the floor
Stores run lean, and new associates lack deep product knowledge, making it hard to restock, fulfill online orders and still give shoppers helpful advice.
-
CH-04
Disconnected omnichannel data
POS, ecommerce, loyalty and order management systems often hold separate customer and inventory views, blocking consistent service and accurate decisions across channels.
03 Use cases
AI use cases for retail.
Proven applications we design, build and run in production — each delivered through one of our core services.
Computer vision shelf monitoring
Fixed cameras or handheld scans detect empty facings, misplaced items and planogram gaps, creating restock tasks while focusing on shelves and products rather than shopper identity.
Computer VisionStore associate knowledge copilot
A handheld assistant answers product, policy and stock questions from your catalog and SOPs, checks nearby store availability and helps new associates serve customers confidently.
AI Copilot DevelopmentOrder routing and fulfillment optimization
Models choose which store or distribution center should fulfill each online order based on inventory confidence, labor capacity and delivery promises, reducing cancellations and split shipments.
ML DevelopmentConsented loyalty personalization
Recommendation models tailor offers and emails using opted-in loyalty history, with transparent preference controls and no individualized price discrimination between customers.
AI DevelopmentEdge AI for in-store analytics
On-premise edge devices process camera and sensor data locally for queue length and footfall counts, sending only aggregated metrics to the cloud to limit privacy exposure.
Edge AI & IoT04 Services
AI services for retail.
Predictive Analytics
Forecasting, churn, demand and risk models.
Learn more
Computer Vision
Image and video AI for detection, inspection and recognition.
Learn more
AI Copilot Development
In-product AI assistants that work alongside your users.
Learn more
ML Development
Custom machine learning models for prediction and classification.
Learn more
Edge AI & IoT
On-device AI for cameras, sensors and embedded hardware.
Learn more
Data Engineering for AI
Pipelines, vector stores and feature stores that feed AI.
Learn more
05 How we deliver
From use case to production.
Every retail AI project follows the same five stages, so you validate value early and scale with confidence.
-
01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
-
02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
-
03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
-
04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
-
05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
06 Built for your sector
Designed for retail realities.
Data, regulation and the systems you already run shape every architecture decision we make.
Shopper privacy and consent
We design around GDPR, CCPA and state biometric privacy requirements, avoid facial recognition of shoppers, process video at the edge where possible, and use loyalty data only within the consent customers have given.
POS, ERP and OMS integration
Our systems connect to point-of-sale, ERP, warehouse and order management platforms plus planogram and workforce tools, so insights arrive inside the replenishment and tasking flows teams already use.
Store variability and peaks
Store formats, connectivity and staffing differ widely, and holiday peaks stress every system, so we build models that adapt per location, run reliably on store hardware and keep working offline.
How is AI used in retail stores?
Common uses include store-level demand forecasting, automatic replenishment suggestions, shelf monitoring to catch empty facings, associate assistants for product questions, and optimized routing for online orders fulfilled from stores. The best results come from connecting AI to existing POS, inventory and tasking systems so recommendations turn into actions on the shop floor.
Does shelf monitoring AI track shoppers?
It does not need to, and we design to avoid it. Our shelf analytics detect products, gaps and price labels, and in-store analytics produce aggregated counts rather than identifying individuals. Processing on edge devices keeps raw video in the store, and we help you post clear signage and follow applicable privacy and biometric requirements.
Can AI improve inventory accuracy for buy online, pick up in store?
Yes. Models can estimate the confidence that system stock actually exists on the shelf, combining sales velocity, shrink patterns, scan data and shelf images. Order management can then route orders to stores most likely to fulfill them, and flag counts that need verification, reducing cancellations and disappointed customers.
Will AI replace store associates?
Our focus is helping associates, not replacing them. AI handles repetitive work such as spotting gaps, prioritizing restock tasks and answering policy lookups, freeing staff to help shoppers and keep stores running. Store managers keep control over staffing, ordering and customer decisions, with AI suggestions they can accept or override.
How do you personalize retail offers responsibly?
We use loyalty and purchase data only where customers have opted in, give them clear controls over preferences, and avoid inferring sensitive traits. Personalization shapes which products and offers are shown, not charging different customers different prices for the same item. Regular reviews check recommendations for unintended bias across customer groups.