A Retail Intelligence Platform to Turn Data into Prioritised Store Actions
Challenges
Manual analysis slowed operational decision making.
Reports showed what happened, not why.
Disconnected data made prioritisation difficult.
Outcome
2X faster response to operational issues.
10–15% reduction in manager admin effort.
More consistent, data driven decisions across stores.
Solution
Retail Intelligence Platform
Challenges
Solution
Technology Stack
Outcomes
A multinational UK based brand was building a Retail Intelligence Platform to help its store managers make faster decisions about sales, product availability, and waste. The data already existed. The challenge was turning it into action. Managers still had to move between reports, analyse operational information and use their experience to understand what had gone wrong and what to do next.
Cloudaeon built this solution to automate that process. The platform identifies operational anomalies, investigates likely root causes and converts the findings into prioritised actions for store managers. Instead of giving managers another dashboard to interpret, the platform tells them where attention is needed, why and what action to consider next.
Challenges
Too much time was spent investigating data: Store managers had access to sales, stock, waste and other operational information, but turning it into action required significant manual effort. They had to gather information, query data, identify unusual patterns and decide what action to take.
Reporting showed what happened, but not why: Existing reporting and analytics tools could highlight performance changes, but they did not automatically explain the underlying cause. A manager might see that a department was underperforming but still need to investigate stock levels, shelf availability, replenishment activity and fulfilment before deciding what to do.
Decisions depended heavily on individual experience: Managers combined available data with their training and local knowledge. But it made decision making difficult to standardise across hundreds of stores. Complex patterns could also be difficult to identify consistently through manual analysis.
Relevant information was spread across multiple operational areas: Understanding a single issue could require information from multiple teams. Connecting these signals manually made root cause analysis slow.
More automation could not mean more alerts: Simply detecting more anomalies would not solve the problem. Managers needed a manageable number of relevant actions. The platform therefore had to validate findings, remove duplication and prioritise issues based on their business impact and urgency.
Root Cause Analysis
Cloudaeon did not start by building another reporting layer. We first examined how a store manager moved from identifying a performance issue to deciding what action to take. The gap existed between data and the decision a manager needed to make. The existing system did not consistently connect a performance anomaly with the operational signals that could explain it.
Solution
Cloudaeon designed and built a retail intelligence platform on Microsoft Azure and integrated it with the organisation’s enterprise data ecosystem. The platform continuously evaluates operational KPIs, identifies anomalies and investigates their likely causes. Agent-based workflows query relevant business data and perform root cause analysis. Enterprise LLMs then translate the findings into clear explanations and recommended actions. A task orchestration validates, prioritises and filters those recommendations before they reach store managers through the task management platform and handheld devices. Managers can accept an action, modify it using their local knowledge or provide additional information specific to their store, area or department.
How We Delivered
Built the Operational Data and KPI Foundation: Cloudaeon brought together operational information covering multiple teams. Automated ingestion pipelines, scheduled refresh patterns, reconciliation checks and data quality controls prepared the information for analysis. The platform then calculated and maintained the KPIs required by the downstream intelligence workflows. This ensured the decision process began with validated business metrics rather than asking an LLM to calculate or infer them.
Created Deterministic Anomaly Detection: A Databricks anomaly detection pipeline monitors operational and commercial KPIs. The solution uses business rules, configurable thresholds, historical trends and peer group comparisons to identify exceptions, including:
Sales underperformance
Stock availability issues
Fulfilment failures
Waste compliance concerns
Forecasting mismatches
Customer experience trends
When a KPI deviates from expected performance, the platform generates an anomaly event for investigation.
Routed Anomalies into Automated Triage: Anomaly events are passed through Kafka to channels subscribed to the relevant input topics. From there, each anomaly enters the triage workflow. This separates two different engineering problems: identifying that something is wrong and determining why it is happening. Only after the anomaly has been identified does the deeper investigation begin.
Built Agent-Based Root Cause Analysis: The triage workflow identifies the data required to investigate each anomaly. Agents are given specific skills to perform different parts of the analysis. The SQL skill allows the agent to query relevant data tables and retrieve the information needed for investigation. The RCA skill provides the process for analysing that information and identifying likely root causes.
Added LLM Reasoning After the Analytical Process: Enterprise grade LLMs are hosted through the client's AI platform and accessed through an AI gateway. The LLM is used after the anomaly has been detected and investigated. Its role is to:
Interpret root cause findings
Convert technical findings into plain English
Explain what appears to have happened
Recommend the next appropriate action
Provide supporting rationale for the recommendation
This allowed us to combine deterministic analytics with AI assisted interpretation without relying on the LLM for core KPI calculations or anomaly detection.
Generated and Prioritised Actions: An Action Item Generation skill combines investigation results with relevant store and stock information to create potential actions. Each action is assigned a priority. Before an action reaches the store, the task orchestration layer validates the anomaly, adds business context, removes duplicates, evaluates impact and urgency and controls task volumes. This prevents managers from being overwhelmed by alerts.
Delivered Recommendations Directly into Store Operations: Prioritised actions are surfaced to store managers through their handheld devices. Managers can review the issue and recommendation without first navigating multiple reporting systems. They can accept the suggested action, modify it based on local knowledge or add information specific to their store, department or area. The system therefore supports the manager's judgement rather than attempting to replace it.
Added Governance, Monitoring and Traceability: The solution was designed for enterprise scale adoption. Governance, monitoring, observability, auditability and feedback mechanisms provide traceability across anomaly detection, root cause investigation, AI reasoning, recommendation generation and task execution. The standardised logic also creates a foundation for applying the same approach across the stores and extending the platform into additional operational use cases.
Technology Stack
Microsoft Azure
Databricks
Apache Kafka
Enterprise LLMs
AI Gateway
AI agents
Outcome
2X faster action on operational issues through prioritised and root cause backed recommendations.
Approximately 10% to 15% reduction in manager administrative efforts.
Reduced time spent manually analysing reports and investigating performance issues.
Consistent decision making across stores through standardised anomaly detection and triage.
Better visibility into sales, product availability and customer feedback.
Conclusion
Organisations already have the operational data. The challenge is turning it into consistent action without asking managers to spend hours investigating reports. Cloudaeon builds Intelligent Platforms to automate that path. Deterministic analytics identify the issue. Agent-based triage investigates the cause. AI translates the findings into a clear recommendation. Task orchestration ensures managers see the actions that deserve their attention. The result is an enterprise intelligence capability that helps teams spend less time analysing operational data and more time acting on it.
If your operational teams can see what is happening but still spend hours working out why it happened and what to do next, Cloudaeon can help turn that data into a governed decision and action workflow. Talk to an expert now.