Overview

Managing product data at scale is one of the most challenging problems in modern commerce. Large catalogs, frequent updates, and inconsistent vendor inputs make it difficult to maintain accurate and structured product information. This case study explains how a quick-commerce organization used agentic AI with large language models (LLMs) to automate product attribute extraction and title standardization — turning a manual, error-prone process into a scalable, production-ready system.

The outcome was a smarter product knowledge base that improved efficiency, data quality, and customer experience across platforms.

The Challenge: Product Data That Doesn’t Scale

In a fast-moving commerce environment, thousands of products arrive from multiple vendors, each using different naming conventions and formats. Important details such as brand, flavor, volume, or packaging are often missing, inconsistent, or hidden inside long titles and images.

Previously, ensuring data accuracy depended heavily on manual review. While this worked at small scale, it quickly became:

• Time-consuming and expensive
• Difficult to scale across regions and catalogs
• Prone to inconsistencies and human error

A more intelligent and automated approach was required — one that could reliably understand product information and standardize it without slowing down operations.

Why Structured Product Attributes Matter

Structured attributes are not just about cleaner data; they directly impact business performance:

• Improved Search & Filtering Customers can easily find products using precise filters such as brand, size, or flavor.
• Smarter Recommendations Products sharing similar attributes can be recommended more accurately based on user behavior.
• Stronger Analytics & Merchandising Insights Structured data enables better trend analysis, category performance tracking, and data-driven decision-making.

Without high-quality structured attributes, discovery, personalization, and analytics all suffer.

Leveraging LLMs for Product Understanding

Recent advancements in LLMs, especially multimodal models that understand both text and images, made it possible to automate this challenge. These models can interpret vendor titles and product images, extract meaningful attributes, and even rewrite product titles into a consistent, standardized format.

Instead of relying on brittle rules or templates, LLMs brought the flexibility needed to handle real-world, messy product data.

Choosing an Agentic Architecture

Several architectural approaches were evaluated:

• Single-turn LLM calls — Simple but insufficient for multi-step workflows
• Predefined agents — Multiple LLM steps orchestrated in a fixed sequence
• Dynamically orchestrated agents — More flexible but higher cost and complexity

Because the problem naturally breaks into two clear steps — attribute extraction followed by title generation — a predefined agent approach was selected. This ensured predictability, lower latency, easier debugging, and controlled costs for a business-critical workflow.

How the Agents Work Together

The system consists of two coordinated LLM agents:

1. Attribute Extraction Agent
2. Title eneration Agent

Each agent has a focused responsibility, making the system easier to maintain and improve over time.

Optimizing for Performance, Cost, and Quality

To make agentic AI viable in production, several optimizations were applied:

Prompt Engineering

• Prompts were refined to be concise and unambiguous
• Reduced token usage lowered latency and operational costs
• Clear instructions improved consistency and accuracy

Knowledge Distillation (Teacher–Student Approach)

• A large, powerful model generated high-quality training examples
• These examples were used to fine-tune a smaller, more efficient model
• The smaller model achieved similar output quality with faster responses and lower cost

Confidence Scoring & Human Oversight

• Each output was assigned a confidence score
• Low-confidence predictions were automatically flagged
• Human review ensured quality for edge cases

This balance allowed automation at scale without sacrificing trust.

Results and Impact

By adopting an agentic AI approach, the organization achieved:

• Significant reduction in manual catalog work
• More accurate and consistent product data
• Improved search and recommendation quality
• Better customer experience in product discovery

Most importantly, the system scaled reliably as catalogs and regions expanded.

Key Takeaways

• Agentic AI works best when complex tasks are broken into clear steps
• Predefined agents provide stability and predictability for core workflows
• Cost and latency must be optimized alongside model intelligence
• Human-in-the-loop mechanisms remain essential for quality assurance

Smarter Product Data at Scale

This case study demonstrates how agentic AI can move beyond experimentation into real production impact. By combining LLMs, predefined agents, performance optimization, and human oversight, the organization built a scalable product knowledge base that supports better discovery, personalization, and analytics.

It’s a strong example of how agentic AI delivers real value when designed with structure, efficiency, and trust — not just intelligence.

From Conversational Data to Real-World AI Impact

At ElevateTrust.ai, we build AI systems that go far beyond dashboards, demos, and proofs of concept — into production-grade, business-critical deployments.

We help organizations turn AI vision into execution through:

• AI-powered Video Analytics & Computer Vision
• Edge AI, Cloud, and On-Prem deployments
• Custom detection models tailored to industry-specific needs

From attendance automation and workplace safety to intelligent surveillance and monitoring, our solutions are designed to operate reliably in real-world environments — where accuracy, latency, and trust truly matter.

Just as conversational AI agents are transforming how teams interact with data, we focus on building AI systems that understand context, scale confidently, and deliver measurable business outcomes.

Book a free consultation or DM to get started  https://elevatetrust.ai

Let’s build AI that doesn’t just watch — it understands.