Translates business questions into accurate SQL queries, eliminating the need for analysts to manually write data access code for every executive request.
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About the Client
Our client is a UK-based luxury jewelry and timepieces brand. Known for their craftsmanship and heritage, they serve global customers with a D2C online ordering system that minimizes reliance on third-party marketplaces.
Client Location
UK
Engagement Model
Dedicated Team
Project Duration
Aug 2022 – Ongoing
Industry
Luxury eCommerce
Project Goals
The client was facing a critical business bottleneck where senior executives were unable to access real-time data without submitting requests to the analytics team. This resulted in 48 to 72-hour delays for standard reports. With that, strategic decisions were reactive rather than proactive. The client wanted to empower their leadership team to independently access business insights on demand, accelerate decision-making cycles, and free their analytics team from repetitive reporting tasks to focus on strategic initiatives that drive competitive advantage. We leveraged our hands-on experience in developing custom tech solutions for the retail industry to help them achieve these goals.
Brief of the Project
The client's sales, inventory, and customer data lived across siloed systems. Accessing insights meant submitting requests to analysts, creating bottlenecks that delayed strategic decisions. Executive dashboards were static, and a small analytics team couldn't keep pace with business demand for real-time reporting and ad-hoc queries.
We integrated a conversational AI agent into their business intelligence platform, with which executives can now ask luxury retail analytics-related queries in plain English and receive instant, structured answers. Using an LLM integrated with Snowflake, we ensured conversational data querying and intelligent interpretation of the client's database schema. The natural language queries are thus translated directly into optimized SQL without analyst intermediaries.
The platform includes a chat-based analytics assistant, custom Snowflake integration for multi-step queries, and feedback loops that continuously improve accuracy. The modular architecture scales as data grows, absorbs new schemas without retraining, and maintains full audit compliance and enterprise security governance.
Client Feedback
The AI agent understands our business terminology, SKU categories and regional demand patterns, and seasonal trends better than expected. We've gone from waiting 3 days for reports to getting insights in minutes. It has genuinely transformed how we make decisions in this business.
James Hartley
Head of Commercial Operations
This wasn't just about bolting an LLM onto a database. The real challenge was teaching the AI agent to understand the luxury retail business. We built context-aware query generation that maps business semantics to data reality and wraps it in a feedback loop so the model learns from every interaction. The magic is in this translation layer between how humans think and how databases work.
Hardik Patel
Solution Architect, Radixweb
Key Challenges
- Executive teams couldn't access real-time business data without submitting requests to analysts, creating bottlenecks that delayed critical strategic decisions by 48-72 hours.
- Sales, inventory, and customer data were scattered across different systems, which made day-to-day reporting work more of a hassle than it needed to be. Analysts were spending hours stitching together reports instead of focusing on strategic analysis.
- The client's luxury customer base and transaction volumes kept growing, so we had to build the enterprise-grade, conversational data analytics solution with a scalable foundation.
- Executives wanted faster insights and easier access to data, and the old static dashboards weren’t keeping up with business expectations for real-time, on-demand reporting.
- Even small updates to the legacy analytics infrastructure had become risky and time-consuming to execute. The analytics team was stuck maintaining old systems instead of building new capabilities.
Solutions Delivered
Conversational Analytics Agent
The LLM-powered, agentic AI data analytics platform understands natural language queries about business metrics, translates them into context-aware SQL, and delivers structured insights. The agent learns from user feedback and refines interpretation over time.
Intelligent Schema Mapping
We built a custom, enterprise conversational AI agent that maintains a dynamic map between business terminology and database schema. The system automatically disambiguates terms and explains query logic to users, ensuring transparency and trust in AI-generated analytics results.
Real-Time Data Integration
A Snowflake-native integration layer connects the agentic AI platform to the data warehouse, enabling sub-5-second query responses. Intelligent caching for high-frequency queries and cost-optimization logic were implemented for routing complex aggregations.
Azure-Powered Orchestration
Azure AI Foundry implementation ensured scalable model serving, multi-turn conversation management, and seamless orchestration between NLP processing, SQL generation, and data warehouse queries, for reliability, compliance, and easy upgrades.
Audit-Ready Security Framework
Implemented comprehensive query logging, data governance policies, and Role-Based Access Control (RBAC) in the AI platform to ensure access to relevant insights only. Every query is auditable, traceable, and compliant with data protection regulations.
Feedback-Driven Learning Loop
Created a mechanism for executives to rate query results, flag misinterpretations, and suggest clarifications for the retail analytics platform. This feedback continuously refines the AI agent's accuracy, thus, improving query interpretation and building confidence in the platform over time without manual retraining.
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Technologies Used
Large Language Model (LLM)
Azure AI Foundry
Orchestrates multi-turn conversations and manages the AI model at enterprise scale, so the platform runs reliably without custom infrastructure overhead.
Snowflake Data Warehouse
Executes complex natural language queries efficiently at scale, maintaining sub-5-second response times even during peak e-commerce traffic and high query volume.
Custom Query Interpretation Engine
Maps business terminology directly to database schemas and validates SQL before execution, reducing hallucination and catching query errors before they hit the database.
Role-Based Access Control
Enforces row-level security and logs every query for compliance, ensuring executives only access relevant data while maintaining full traceability for AI governance, security, and auditability.
Chat Interface & Conversational UX
Delivers analytics through a familiar chat experience requiring zero training, enabling rapid adoption across non-technical executives without IT or specialized data analytics support needed.
Business Benefits
Decision-Making Speed
The custom-engineered AI capabilities for instant insight access reduced insight delivery time from 48-72 hours to sub-5 seconds. Executives can now react to market trends, inventory challenges, and performance issues within minutes, enabling faster pivots in competitive luxury retail markets.
Operational Cost Reduction
Freed up 60% of the analytics team's capacity previously spent on manual reporting. The team now focuses on strategic data initiatives, model building, and business intelligence architecture, all higher-value work that drives competitive advantage, not routine query fulfillment.
Executive Empowerment
Non-technical executives gained independent access to business data without IT intermediation. User adoption exceeded 85% within six months (45% increase over traditional BI dashboards), because natural language data analytics felt intuitive even without training.
Future-Proof Enterprise Scalability
The modular, AI-agent-based architecture easily absorbs new data sources, business domains, and schema changes without rebuilding dashboards. As the client's eCommerce operations expand globally, the conversational analytics platform can scale without proportional infrastructure investment or technical debt.
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