More Revenue from Personalisation — for Faster-Growing Retailers
Lower Supply Chain Costs and Better Availability with AI
Retail operations / margin & cost
Why work with us
AI and Data Experts on board
Finished projects
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
What our clients say
The entire team has so much to be proud of – driving up efficiency, quality and timeliness, and I (and the entire Medidata team) certainly appreciate the collaboration needed to achieve all of this.
Craig Stauffer – Medidata Senior Director, Implementation Services – Medidata Solutions
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Clients that trusted us
Retailers generate extraordinary amounts of data — transactions, customer behaviour, inventory movements, pricing signals — yet most of it sits fragmented across systems that were never built to communicate. Only one in three companies actually extracts measurable economic value from AI. The gap is rarely technological. It is organisational — weak data foundations, siloed systems, and a shortage of partners who can carry a solution from proof-of-concept to production.
Addepto was built for that gap. We combine custom AI development with data engineering to turn fragmented data into production-ready decisions: sharper forecasts, deeper personalisation, smarter pricing, leaner operations. We don’t come in as a vendor. We embed alongside your team, identify the processes where automation will genuinely move the needle, and build systems that stay in production long after launch.
Data-first. Implementation-proven. Production-grade.
Customer Personalisation at Scale
Demand Forecasting & Inventory
Pricing & Promotion Intelligence
Computer Vision for Store Operations
How AI Supports Retail & E-Commerce
Higher Margins and Revenue Per Visit
When recommendations are built on actual purchase history and browsing behaviour, shoppers find what they want faster — and buy more. Personalisation integrated directly with your e-commerce platform and CRM turns every visit into a more relevant, higher-value interaction, and makes promotional spend work harder by targeting the customers most likely to convert
Fewer Stockouts, Lower Carrying Costs
Every stockout is a lost sale and a frustrated customer. Every overstock is capital sitting in a warehouse. Accurate, store-level demand forecasting means replenishment decisions are driven by what is actually selling — reducing the cash tied up in excess inventory and keeping shelves stocked where it matters most.
Leaner, Faster Store Operations
A regional manager overseeing dozens of stores cannot physically verify shelf compliance across all of them. Computer vision does it continuously — flagging gaps, planogram deviations, and out-of-stocks in real time, so teams spend less time auditing and more time fixing. The hours saved compound quickly across a large store estate.
Decisions Made on Current Reality
By the time a weekly report lands, the situation it describes has already changed. A unified data platform gives merchandisers, planners, and operations teams live signals to act on — so markdowns happen before overstock deepens, promotions are adjusted while they are still running, and restocking decisions reflect today’s demand, not an estimate made days ago.
Retail and E-Commerce Challenges Solved with AI
How do you protect margin when competitors change prices daily and demand shifts hourly?
Challenge: Most retailers plan promotions on historical templates and gut feel, with post-campaign analysis arriving weeks too late to change anything. Promotional spend is one of the largest controllable cost lines in retail — and most of it is never properly measured.
Solution: Addepto builds AI models that predict promotional uplift before a campaign launches and adjust recommendations while it is still running — accounting for price elasticity, cannibalisation, and basket composition. Merchandising teams finally see which mechanics drive incremental revenue and which drive substitution.
How do you stop losing sales on fast movers while sitting on excess inventory in the same warehouse?
Challenge: Orders are placed on last season’s sales and adjusted for gut feel — so one SKU (Stock Keeping Unit) sells out across all stores while its adjacent variant piles up in the DC (distribution centre). The demand signals to prevent this exist; they are just never connected fast enough to drive the right replenishment decision.
Solution: Machine learning forecasting models built at the individual product, store, and daily level pull live signals from POS, e-commerce, and external demand indicators. Automated replenishment recommendations connect directly into your ERP (Enterprise Resource Planning) system — so inventory allocation reflects what customers are buying today, not last month.
How do you act on retention signals before a customer is already gone?
Challenge: Retailers can see churn trends in aggregate — declining basket size, dropping loyalty redemption, lower visit frequency — but aggregate data cannot tell you which customers are drifting, what triggered it, or what offer would bring them back. By the time a report lands, the window to act has closed.
Solution: Addepto builds customer intelligence models that score churn risk at the individual level and trigger personalised retention actions via loyalty, email, or in-app at the right moment. The output is not a report — it is an automated workflow that acts while there is still time.
How do you give store and regional teams real visibility — without adding to their workload?
Challenge: Shelf gaps, planogram non-compliance, and out-of-stocks are only caught when someone physically walks the floor — by which point the lost sales have already happened. None of it appears in the weekly report until it is too late to fix.
Solution: Addepto deploys computer vision systems that monitor shelves, detect out-of-stocks, and check planogram compliance in real time — flagging issues directly to store and regional managers as they happen, not after the fact.
How do you protect margin when competitors change prices daily and demand shifts hourly?
Challenge: A weekly pricing review cycle cannot keep pace with competitors moving prices continuously across marketplaces and online channels. Retailers end up under-cutting themselves on high-demand lines or holding prices too long on slow movers — compressing margins in both directions.
Solution: Addepto builds dynamic pricing engines that monitor competitor pricing, demand velocity, and inventory levels simultaneously to recommend optimal prices across your full assortment. Markdown timing is automated around sell-through targets — so clearance happens before overstock deepens.
How do you protect margin when competitors change prices daily and demand shifts hourly?
Challenge: Most retailers plan promotions on historical templates and gut feel, with post-campaign analysis arriving weeks too late to change anything. Promotional spend is one of the largest controllable cost lines in retail — and most of it is never properly measured.
Solution: Addepto builds AI models that predict promotional uplift before a campaign launches and adjust recommendations while it is still running — accounting for price elasticity, cannibalisation, and basket composition. Merchandising teams finally see which mechanics drive incremental revenue and which drive substitution.
How do you stop losing sales on fast movers while sitting on excess inventory in the same warehouse?
Challenge: Orders are placed on last season’s sales and adjusted for gut feel — so one SKU (Stock Keeping Unit) sells out across all stores while its adjacent variant piles up in the DC (distribution centre). The demand signals to prevent this exist; they are just never connected fast enough to drive the right replenishment decision.
Solution: Machine learning forecasting models built at the individual product, store, and daily level pull live signals from POS, e-commerce, and external demand indicators. Automated replenishment recommendations connect directly into your ERP (Enterprise Resource Planning) system — so inventory allocation reflects what customers are buying today, not last month.
How do you act on retention signals before a customer is already gone?
Challenge: Retailers can see churn trends in aggregate — declining basket size, dropping loyalty redemption, lower visit frequency — but aggregate data cannot tell you which customers are drifting, what triggered it, or what offer would bring them back. By the time a report lands, the window to act has closed.
Solution: Addepto builds customer intelligence models that score churn risk at the individual level and trigger personalised retention actions via loyalty, email, or in-app at the right moment. The output is not a report — it is an automated workflow that acts while there is still time.
How do you give store and regional teams real visibility — without adding to their workload?
Challenge: Shelf gaps, planogram non-compliance, and out-of-stocks are only caught when someone physically walks the floor — by which point the lost sales have already happened. None of it appears in the weekly report until it is too late to fix.
Solution: Addepto deploys computer vision systems that monitor shelves, detect out-of-stocks, and check planogram compliance in real time — flagging issues directly to store and regional managers as they happen, not after the fact.
How do you protect margin when competitors change prices daily and demand shifts hourly?
Challenge: A weekly pricing review cycle cannot keep pace with competitors moving prices continuously across marketplaces and online channels. Retailers end up under-cutting themselves on high-demand lines or holding prices too long on slow movers — compressing margins in both directions.
Solution: Addepto builds dynamic pricing engines that monitor competitor pricing, demand velocity, and inventory levels simultaneously to recommend optimal prices across your full assortment. Markdown timing is automated around sell-through targets — so clearance happens before overstock deepens.
AI Solutions for Retail
How do you turn customer data into revenue-driving decisions?
We build recommendation engines that analyse browsing behaviour, purchase history, loyalty signals, and contextual data to surface the right products at the right moment — across web, app, and email. Beyond product suggestions, our customer intelligence models identify churn risk, high-value segments, and cross-sell opportunities, giving your marketing and CRM teams an AI-powered edge.
How can you predict demand without relying on spreadsheets?
Forecasting models built on your actual sales history — layered with promotional calendars, seasonality patterns, and external signals like weather and local events — generate predictions at the level of individual products, stores, and days. Those predictions feed directly into replenishment and procurement workflows, replacing spreadsheet-driven planning with continuous, automated decisions that reduce both stockouts and excess inventory at the same time.
How do you balance competitive pricing with margin protection?
AI models analyse competitor pricing, price elasticity, demand patterns, and inventory levels in real time to recommend optimal prices and discount structures. Whether you need markdown optimisation for seasonal clearance or dynamic pricing across thousands of individual product lines, we build systems that protect margin while staying competitive — and explain every recommendation to the business.
How can you gain real-time visibility into in-store execution?
Our computer vision solutions automate shelf compliance checking, planogram adherence monitoring, product presence validation, and in-store traffic analysis. Built for non-technical users with intuitive dashboards, these systems replace manual store audits with real-time, actionable visibility.
What does scalable, always-on customer support look like today?
From AI chatbots that handle routine queries 24/7 to product discovery assistants and post-purchase support agents, we build conversational AI solutions that reflect your brand voice and integrate with your existing CRM and order management systems. As buyer expectations shift toward agentic commerce, we ensure your customer-facing AI is ready to handle more complex, multi-step interactions
What our clients say
Addepto has an individual approach from the very beginning. They are open to change and ready to face difficulties.
Bobby Newman VP Engineering – J2 Global
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
We are recognized as one of the best AI, BI, and Big Data consultants
We helped multiple companies achieve their goals, but - instead of making hollow marketing claims here - we encourage you to check our Clutch scoring.
FAQ
Where do we start with AI in retail?
The most effective starting point is a use case that has a clear business KPI attached to it — typically demand forecasting, personalisation, or store operations. Addepto runs structured discovery workshops to identify where your data and operational readiness supports the highest-impact AI deployment first, so you invest in what will deliver measurable returns quickly.
Our data is messy and siloed. Can we still implement AI?
Data quality is the most common blocker — and the one we tackle first. Our data engineering practice assesses your current data estate, implements the necessary integration, cleansing, and governance layers, and builds the platform that makes AI feasible. You do not need perfect data to start; you need the right partner to build toward it.
How long does it take to go from pilot to production?
We use a phased approach: validate the use case with a focused PoC, prove the ROI with a production pilot in one category or geography, then scale across the organisation. Each phase has clear success metrics agreed upfront
How do you work with our internal IT, data, and product teams?
We work as an extension of your team. Our engagement model includes joint workshops, shared documentation, code handovers, and team training. Addepto’s goal is always to build internal capability alongside the solution, so your team can own and evolve the AI system over time.
How do you handle data privacy and AI governance?
Retail data includes sensitive customer and transactional information. Addepto builds with privacy-by-design principles, implements access controls and audit trails, and ensures all AI outputs are explainable and auditable. We align every project with GDPR and relevant regional data protection frameworks, and we can advise on responsible AI practices for pricing, personalisation, and automated decision-making.
How do we build a business case and measure ROI?
We define success metrics at the start of every engagement — tied to real operational KPIs such as forecast accuracy, stockout rate, conversion uplift, or cost-per-store-audit. Progress is tracked throughout delivery, and we support you in presenting AI outcomes to executive and board-level stakeholders. The goal is not a working model — it is a business result you can measure and scale.
