Achieve Retail Excellence with an AI-Ready Data Foundation

AI pilots rarely fail because of the models. They fail because the data behind them isn’t built for scale. We help retailers strengthen their data foundation so AI can drive faster decisions, higher margins, and more efficient operations.

The Revenue Gap Between Insight and Action

Build an AI-ready data foundation designed explicitly for value realization, supporting your teams with embedded intelligence for demand forecasting, replenishment, pricing strategy, customer experience, and more.

What’s Happening Today	With the Right Data Foundation	What You Gain
“We forecast demand accurately, but actions come too late.”	Pricing and replenishment decisions adjust automatically based on live demand signals.	Higher sell-through. Fewer stockouts. Protected margins.
“We know demand will spike, but supply reacts too slowly.”	Inventory reallocates dynamically across locations.	Improved availability without excess stock.
“Customer feedback sits in silos; it’s unstructured and ignored.”	Multimodal data (reviews, chats, emails, call centres, store interactions) is incorporated into personalization signals.	Higher conversion and repeat purchase rates.
“We don’t know why sales dropped in a specific location or region.”	Business users and AI agents query trusted data directly and simulate scenarios.	Faster decisions. Reduced dependency on analysts.

Where Data Foundations Turn Retail AI Into Results

90% Reduction in Content Production Costs

For a leading e-commerce retailer, we built an AI system that creates on-brand, SEO-optimized product content at scale, fully aligned with accessibility standards, allowing the client to cut costs and accelerate release cycles by 300%.

+20% Sales Forecast Accuracy

For a multinational clothing retailer, we designed and implemented a Machine Learning model, improving sales forecast accuracy by up to 20%, leading to more efficient procurement planning and reducing logistic costs.

Personalized Offers That Cut Returns by 30%

For an e-commerce retailer, we implemented an AI-driven personalization engine that tailors products and offers in real time based on customer behaviour and browsing patterns. By aligning recommendations with intent, the retailer increased customer satisfaction and loyalty while cutting returns by 30%.

AI-Powered Sales Engagement and Intelligence

For a leader in consumer health CPG companies, we’ve built an enterprise-grade data and AI layer on top of the existing CRM, transforming it into an intelligent sales engagement platform for field sales teams, account managers, and sales leadership. This led to improved effectiveness of field sales through AI-driven guidance.
Clear visibility into how sales actions translate into business results.

Demand Forecasting

For a near-airport parking company operating more than 100 facilities, we built an integrated data solution to forecast demand up to 30 days ahead. It combines internal and external data sources, including TSA passenger volumes, airline schedules, weather patterns, and public holidays, providing accurate forecasts for better pricing, staffing, and shuttle planning.

What separates the top 10% of retail disruptors from the rest? It isn't access to a superior LLM; it is the ability to close the gap between data and decision. Differentiation is driven by investments into a multimodal data foundation that senses diverse signals and a governed semantic context that enables true reasoning. When you anchor these to a trusted agentic execution framework across interconnected applications, you bridge the gap between AI hype and measurable ROI.

Oleg Royz, VP Retail & Distribution, DataArt

How Leading Retailers Structure AI

We help retail leaders move beyond passive analytics into real-world action. Sense. Reason. Act. That’s how AI moves from insight to measurable impact.

Sense - Detecting Multimodal Signals

Integrate transactional, operational, and customer data into a unified Data Foundation that reflects real-time business conditions: a spike in local weather forecasts, an out-of-stock image from a shelf camera, a surge in call center complaints, or a competitor's sudden price drop.

Reason - Applying Semantic Context and Logic

Apply business rules, semantic logic, and context so AI understands margin, availability, and risk, and customer intent, not just KPIs or alerts from the data tables. This way, the agentic system determines the best business outcome. For example, an AI agent evaluates: “Do we reroute inventory from a nearby warehouse? Do we temporarily raise the price to throttle demand?” making multi-variable trade-offs in seconds.

Act - Deploying Governed Automation

Activate AI automation with strong governance controls, complete audit trails, and human-in-the-loop oversight. Instead of stopping at recommendations, AI executes actions directly within retail operations while staying within defined guardrails.
For example, AI can automatically adjust digital shelf pricing based on demand signals, trigger localized supply chain reallocations when inventory imbalances appear, or alert store associates through handheld devices to restock high-margin products. Each action is logged, monitored, and governed to ensure transparency, control, and operational accountability.

Build the Data Foundation Your AI Strategy Needs

Retail AI delivers results only when the right foundations are in place. Our services focus on the capabilities required to move from isolated experimentation to scalable AI that supports real business decisions.

Decision-Centric AI Strategy

We help retailers identify the decisions that truly drive revenue, margin, cost, and service levels, then design AI solutions that improve those decisions directly within operational workflows.

What We Do

Map high-value retail decisions and clarify ownership
Prioritize AI investments based on economic impact
Design AI decision loops (sense → reason → act → learn)
Define governance and human oversight models

What You Get

Business case tied to measurable outcomes
Decision-value heatmap prioritized by ROI
AI decision process and governance framework

Multimodal Data Foundation

We design and build scalable retail data platforms that unify structured data with text, images, and voice. This creates the business context AI systems need to support automation, advanced analytics, and agent-driven decisions.

What We Do

Design lakehouse or data mesh architectures with multimodal support
Integrate transactional data with customer feedback, images, calls, and chat
Build ingestion pipelines for structured and unstructured data
Implement vector databases and semantic retrieval for AI applications
Establish data quality, metadata, lineage, and privacy standards

What You Get

Multimodal retail data architecture blueprint
Integrated ingestion pipelines across all data types
Vector database and semantic search capabilities
Unified data quality and governance framework

Semantic Context for Retail AI

We create a shared business context so both teams and AI agents interpret data consistently. By combining retail domain knowledge with enterprise data engineering, we enable trusted conversational analytics and agent-assisted decision-making.

What We Do

Design semantic data layers aligned to retail domains
Establish governed business glossaries and standardized definitions
Create agent-ready data products for analytics and AI applications
Implement guardrails for accuracy, permissions, and explainability

What You Get

Consistent, reusable business definitions across systems
Natural-language query and analytics interfaces
Frameworks for trust, explainability, and secure data access
Conversational BI and agent-enabled analytics solutions

Scaling AI: MLOps and Business Controlled Execution

We help retailers move beyond experimentation by operationalizing AI models and agents with the reliability, governance, and controls required for production environments.

What We Do