Trusted by retail and commerce leaders across the globe Our clients
Retail AI consulting services to help you adopt the tools delivering measurable results
As a retail AI consulting and development company, N-iX has over 23 years of experience working with global retail enterprises. We combine our retail development services with our AI consulting services so you get a partner who understands both your retail systems (POS, ERP, WMS, CRM) and how to get AI running reliably inside them. Our cloud engineering, IoT, Data Science, Big Data, and other tech expertise has helped clients build solutions that improved sales forecasting accuracy by 50%, cut operational costs by 10x through tech modernization, and doubled the speed of price processing.
With our proprietary APEX framework , we follow the Pragmatic AI approach towards agentic and generative AI consulting for retail across development and business processes. We help you measure your AI first, then scale it. This strategy allows retail clients to identify what's working in their current AI ecosystem, stop funding what doesn't yield expected KPIs, and build AI-augmented processes that solve business challenges.
Our retail AI consulting services to cover your business needs Your needs. Our solutions.
Business challenge
Service that solves it
- Stockouts on fast-moving items and overstock on slow-moving ones, both of which erode margin
- Demand forecasting and inventory optimization: Predictive models for stock allocation, replenishment, and promotional campaigns
- Manual or delayed price changes that lose sales to competitors or leave margin on the table
- Competitive pricing intelligence and dynamic pricing models: Near real-time competitor price tracking feeding pricing algorithms
- Return fraud, promo abuse, and chargebacks that scale faster than a manual review team can track
- Fraud detection and loss prevention: Models trained to flag return abuse, promo fraud, and chargeback patterns before they’re approved
- Manual shelf audits, undetected out-of-stocks at the shelf level, shrinkage not caught until inventory counts
- Computer vision for in-store operations: Shelf monitoring, planogram compliance, automated label recognition
- Prior AI initiatives that stalled or never reached production because the data foundation wasn’t there
- AI/data readiness assessment: Data quality and infrastructure audit across POS, ERP, WMS, and CRM before any model is built
- Difficulty identifying which customers are at risk of churning or which segments respond to which offers
- Customer analytics and retention modeling: Segmentation and behavioral analysis for loyalty and marketing
Business challenge
- Stockouts on fast-moving items and overstock on slow-moving ones, both of which erode margin
- Manual or delayed price changes that lose sales to competitors or leave margin on the table
- Return fraud, promo abuse, and chargebacks that scale faster than a manual review team can track
- Manual shelf audits, undetected out-of-stocks at the shelf level, shrinkage not caught until inventory counts
- Prior AI initiatives that stalled or never reached production because the data foundation wasn’t there
- Difficulty identifying which customers are at risk of churning or which segments respond to which offers
Service that solves it
- Demand forecasting and inventory optimization: Predictive models for stock allocation, replenishment, and promotional campaigns
- Competitive pricing intelligence and dynamic pricing models: Near real-time competitor price tracking feeding pricing algorithms
- Fraud detection and loss prevention: Models trained to flag return abuse, promo fraud, and chargeback patterns before they’re approved
- Computer vision for in-store operations: Shelf monitoring, planogram compliance, automated label recognition
- AI/data readiness assessment: Data quality and infrastructure audit across POS, ERP, WMS, and CRM before any model is built
- Customer analytics and retention modeling: Segmentation and behavioral analysis for loyalty and marketing
These are the retail solutions we've built. Here's how AI can make them better.
Competitive pricing intelligence
AI turns static competitor price reports into demand-and-margin predictions, recommending or automatically executing the next price move instead of just showing what competitors charge.
Demand planning
AI folds signals like weather, events, market shifts, and past sales into forecasts, and lets planners trigger replenishment automatically.
Personalized search
AI-powered semantic search matches by meaning and intent rather than exact keywords, cutting the zero-result searches.
Trend search application
We help build AI solutions that understand style and attributes in a photo, not just object category, so they can suggest a close match for the searched item.
CV-powered product placement
AI adds prediction to detection, flagging a shelf with low-stock items before it's actually empty, and allowing for automatic replenishment.
Label recognition
With AI, the system can learn from its own exception queue over time, improving accuracy and decreasing manual review without constant retraining.
Retail and AI reinvention stories from our clients
Where our AI consulting for retail makes a difference Who we serve
What a retailer sells and how they sell it call for different AI systems. We provide retail AI consulting and implementation across both the industries retailers operate in and the channels and models they use to reach customers.
Channels & business models
- Ecommerce and marketplaces
- B2B and wholesale distribution
- Omnichannel and brick-and-mortar retail
- Direct-to-consumer (DTC)
- Subscription and replenishment commerce
- Quick-commerce and on-demand delivery
- Social commerce
Industries
- Grocery and food retail
- Automotive and auto parts retail
- Fashion and apparel
- Big-box and department stores
- Consumer electronics and appliances
- Home and furniture
- Luxury retail
Have a custom AI project?
Technologies and platforms we work with Tech and platforms we work with
What our clients say about us
We know that the people who present us with are usually very very good in terms of embedding into our teams and hitting the ground running.
We're impressed with the value for money they provide.
What surprised me was how far the APEX framework reached beyond engineering. By month four, our business analysts, our QA team, and our developers were all running AI workflows. That's what got us into the top 5% on GenAI adoption within the EQT Group. We're still maturing our AI lifecycle, but N-iX gave us a spec-driven way of working that the rest of the organization could pick up.
We consider them (N-iX team) as our best project not only in terms of quality, but also in terms of methodology and user evaluation.
Why enterprises choose our Artificial Intelligence consulting services for retail
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Pragmatic AI approach for result-driven AI adoption
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Generative and agentic AI for retail consulting capabilities
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10+ active retail clients
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Partnerships with AWS, GCP, Microsoft Azure, Datadog, Palantir, Cursor, and other tech companies
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300+ N-iXers working on retail/ecommerce projects
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Expertise in developing solutions for automotive retailers, international fashion companies, ecommerce businesses, office suppliers, and others
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200 data and 400 cloud certified experts globally
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Tech expertise in cloud, Big Data, ML, Computer Vision, cybersecurity, embedded and IoT solution development, and more
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60+ retail and ecommerce projects delivered
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60 Data Science and AI projects delivered
Tech experts consulting on AI for retail
Valentyn Kropov
Chief Technology Officer
Sergii Netesanyi
Head of Solution Group
Pawel Bulowski
Director, Head of AI Consulting
FAQ
The company providing retail AI consulting services typically starts with a readiness assessment of your data and systems, moves through a scoped Proof of Concept, and then covers the engineering work to integrate the AI system into production tools such as your POS, ERP, or CRM. N-iX runs all three phases.
Retail AI consulting services have to account for retail-specific constraints: SKU-level granularity, seasonal demand, omnichannel inventory, and systems like POS and WMS that a generic AI consultancy may not have integrated before. The retail calendar also shapes timelines, since most retailers won't upgrade core systems during peak season.
It depends on the final goal of consulting. A readiness assessment and prioritized roadmap usually take two to four weeks. A proof of concept for one use case typically runs four to eight weeks. Factors like project maturity, team composition, and tech ecosystem can also influence the duration.
Most projects integrate with point-of-sale, ERP, warehouse management, and CRM systems, as well as ecommerce platforms and data warehouses. The specific integration work depends on what you already run and how clean the data is going in. As a retail AI development company, we at N-iX will help you assess where the AI integration will have the most impact.
We do both, depending on the use case. Some problems are well served by an existing platform or foundation model, with the right data and guardrails in place. Others, particularly forecasting or fraud detection tuned to your own loss patterns, need a custom model. We recommend choosing the one that best fits the use case.
Yes. Many AI consulting projects in retail are built to hand off ownership to an internal data science or engineering team after go-live, and we structure the work with that handover as an explicit milestone rather than an afterthought.
Contact us
Briefly outline your project or challenge, and our team will respond within one business day with relevant experience and initial technical insights.
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