A multi-location retail chain was facing persistent data quality issues stemming from its Point-of-Sale (POS) systems. With tens of thousands of daily transactions, inaccurate product codes, duplicate entries, incomplete records, and mismatched pricing were common. These errors trickled down into inventory planning, sales forecasting, and reporting, creating a lack of trust in enterprise data and increasing the burden on IT and analytics teams. Manual cleaning was time-consuming, reactive, and unsustainable at scale. The retailer needed a way to automate data cleaning in real-time, directly at the source.

The Challenge:

A leading retail chain struggled with data inconsistency and reliability issues across its multi-store operations. With transactions flowing in from diverse POS terminals, e-commerce platforms, and third-party integrations, maintaining a single source of truth became increasingly difficult.

Key challenges included:

• Inconsistent Entries Across Stores: Variations in POS data structures led to discrepancies in daily sales and inventory reports.
• Duplicate Transactions and Discounts: Repeated billing entries and overlapping promotions created confusion in revenue accounting.
• SKU Mismatches: Frequent misalignment between transactional SKUs and master product data caused errors in stock and pricing updates.
• Inaccurate Metadata: Missing or incorrect timestamps, tax codes, and promotional details affected audit accuracy.
• Delayed Reconciliation: Manual data validation and cleansing slowed reconciliation cycles, impacting month-end closings.
• Poor Decision-Making: Unreliable data cascaded into analytics systems, leading to flawed business insights and missed opportunities.

 

The Amantra Solution: RPA + AI for Autonomous Data Cleaning

Amantra deployed a Smart Data Automation Framework that combined the precision of Robotic Process Automation (RPA) with the intelligence of Large Language Models (LLMs) to ensure end-to-end retail data accuracy and consistency.

Key Capabilities:

• Automated Data Ingestion: Digital agents seamlessly collected POS logs from multiple stores and synchronized them with ERP and Product Information Management (PIM) systems.

• Context-Aware Anomaly Detection: Using LLMs, the system understood transactional context to identify discrepancies across SKUs, prices, taxes, and timestamps.

• Automated Data Cleansing: Intelligent agents corrected invalid or missing entries in real time, ensuring alignment with master product and pricing data.

• Exception Management: Only unresolved or unfamiliar anomalies were escalated to human teams with detailed audit trails for transparency.

• Continuous Learning: The AI engine continuously refined its anomaly detection and correction patterns, improving accuracy over time.

This hybrid framework transformed the retailer’s fragmented data ecosystem into a self-healing, trustworthy data foundation, enabling faster reconciliation, better decision-making, and improved operational confidence.

Key Features

Amantra implemented a Smart Retail Data Automation Framework to ensure accuracy, consistency, and reliability across all POS and ERP systems. The solution combined RPA-driven process automation with LLM-powered intelligence to eliminate manual interventions and build a continuously improving data ecosystem.

Key Components:

• POS-to-ERP Sync: RPA-based automation ensured seamless, error-free data flow between POS terminals and ERP systems, eliminating delays and duplication.

• AI-Powered Correction Engine: LLMs identified and corrected data anomalies in real time—covering pricing, SKU mismatches, timestamps, and tax inconsistencies.

• Line-Level Transaction Validation: Automated validation of discounts, promotions, and tax entries ensured compliance and accurate financial reporting.

• Real-Time Dashboards: Provided operational and financial teams with a unified view of transaction health, anomalies, and reconciliation status.

• Closed-Loop Learning: Feedback from business analysts continuously trained the system to recognize new patterns and reduce recurring data issues.

This unified automation layer transformed POS data management into an intelligent, self-learning process, enabling faster reconciliations, improved accuracy, and enhanced decision-making across retail operations.

Business Outcomes

Within the first quarter of implementation, the client achieved measurable improvements in data accuracy, efficiency, and decision-making reliability.

• ↓ 90% reduction in daily data exceptions

• Manual review time reduced from hours to under 10 minutes

• Error propagation across systems virtually eliminated

• Forecast accuracy improved to 93% confidence level

• Data reliability score increased from 65% to 98.7%

 

Client Testimonial

“Amantra helped us move from firefighting to foresight. Clean POS data has improved everything—from reporting to planning to customer experience.” — Director of Retail IT, Multi-City Chain

Why Automating POS Data Cleaning is a Retail Essential

Dirty data at the source leads to poor forecasts, revenue loss, and operational delays. By combining RPA’s speed with AI’s intelligence, Amantra empowers retailers to:

• Identify and correct data issues before they escalate
• Reduce human dependency in data cleanup
• Improve visibility, accuracy, and decision-making

Tired of Fixing Data After It’s Too Late?

With Amantra RPA + AI-powered POS data cleaning agents, you can trust your data, scale operations, and reduce exception handling by up to 90%. Book a Demo | Speak with a Data Automation Expert