One-line Transformation
From fragmented AI workflows to a fully governed, observable, and intelligent retail platform powered by autonomous agents and AI-driven Service Level Objectives (SLOs).
Overview
Retail enterprises are rapidly adopting Agentic AI to deliver personalized shopping experiences and intelligent automation. However, scaling AI into production requires more than intelligent agents it demands complete visibility into agent behaviour, LLM performance, operational costs, and measurable reliability across the AI stack.
To address this, we developed an enterprise Agentic Commerce Platform powered by autonomous AI agents for product discovery, shopping operations, and review intelligence.
The solution was complemented with Datadog AI Observability and a comprehensive AI SLO framework, enabling real-time monitoring, evaluation, guardrails, cost governance, and reliability tracking across every application and AI layer.
Challenges
Business Challenges
- Limited Customer Experience
Traditional keyword-based shopping provided limited personalization, reducing customer engagement and product discovery. - AI Governance & Cost Control
Absence of centralized evaluation, guardrails, AI SLO monitoring, and cost governance increased operational risk for production AI systems.
Technical Challenges
- Complex AI Orchestration
Coordinating multiple AI agents for search, recommendations, shopping operations, and RAG introduced operational complexity. - Lack of AI Visibility & Reliability
Limited insights into agent execution, LLM performance, retrieval quality, latency, token consumption, and the absence of standardized AI Service Level Objectives (SLOs) made it difficult to measure reliability, troubleshoot issues, and maintain production-grade AI performance.
Solution
- Agentic Commerce Platform
Implemented a Supervisor Agent coordinating specialized agents for product search, shopping operations, and customer review summarization. - AI-Powered Intelligence
Leveraged XGBoost for dynamic pricing and Temporal Fusion Transformer (TFT) for demand prediction to optimize business decisions. - Review Intelligence with RAG
Built a FAISS-based knowledge layer enabling AI-generated summaries of customer reviews for faster purchasing decisions. - Enterprise AI Operations, APM & SLO Monitoring
Unified Datadog AI Observability, APM (Application Programs Monitoring), RUM (Real time user Monitoring), and Infrastructure Monitoring to provide end-to-end visibility across AI workflows, application performance, and platform health.
Defined AI Service Level Objectives (SLOs) across the frontend, APIs, gateway, RAG pipeline, LLM services, ML models, infrastructure, and business experience, enabling proactive monitoring of:
- Latency
- Availability
- Retrieval quality
- Model performance
- Token consumption
- User experience
- Business outcomes
Impact Created
- Improved Customer Engagement
Delivered personalized conversational shopping with AI-powered recommendations and intelligent product discovery. - Accelerated Purchase Decisions
Reduced customer effort through AI-generated review summaries and natural language product search. - Optimized Pricing Strategy
Enhanced pricing decisions using AI-driven dynamic pricing and demand forecasting. - Production-Ready AI Operations
Established end-to-end observability, governance, and monitoring for reliable enterprise-scale AI deployments.
Transformation Snapshot
| Feature | Before | After |
| Product Search & Discovery | Keyword-based product search with limited personalization. | Conversational AI-powered product discovery with intelligent, context-aware recommendations. |
| Customer Review Experience | Customers manually reviewed large volumes of feedback before making a purchase. | AI-generated review summaries that enable faster, more informed buying decisions. |
| Pricing & Demand Planning | Static pricing strategies with reactive demand planning (adjusting after changes are observed). | AI-driven dynamic pricing and demand prediction, allowing proactive, data-backed decision-making. |
| AI Workflow Visibility & Operations | Limited visibility into AI workflows, model behaviour, and operational performance. | End-to-end AI observability with tracing, evaluation, guardrails, and cost governance through Datadog, giving full control and transparency over AI systems. |
| SLO’s | None before implementation | SLO’s on LLM services, RAG pipeline, ML Model, Front end etc... |
Conclusion
By combining Agentic AI with enterprise-grade AI Operations, the solution enables retailers to deliver intelligent customer experiences while ensuring AI systems remain observable, governed, and production-ready at scale.
"The true value of enterprise AI lies not just in intelligent automation, but in making every
AI decision transparent, measurable, and trustworthy."
