Why AI Assistants Misrepresent Products and Policies

AI models ingest product catalogs and policy documents separately, but these sources often become unsynchronized over time. When product feeds update but policy texts lag, or vice versa, AI pulls conflicting information. This creates a fundamental disconnect where AI assistants may cite outdated return policies for new products or apply incorrect shipping rules to updated catalog items.

Many e-commerce sites lack standardized schema markup like MerchantReturnPolicy and WarrantyPromise. Without structured data, AI struggles to extract accurate, up-to-date policy facts. Unstructured policy documents force AI to parse natural language, leading to misinterpretation of complex terms, regional variations, and conditional policies.

AI assistants rely on training data or cached website snapshots that become outdated quickly. Without real-time freshness, AI gives obsolete or incorrect answers. Seasonal policy changes, temporary promotions, and inventory updates may not be reflected in AI responses, causing customer confusion and operational issues.

Disparate data silos mean AI aggregates inconsistent information. This leads to contradictory answers about shipping times, return eligibility, or warranty coverage. When product data lives in one system, policy information in another, and inventory status in a third, AI cannot reliably synthesize accurate responses.

Without agentic protocols and traceable data lineage, support leaders cannot easily identify and fix AI errors or catalog-policy mismatches. When AI provides incorrect information, there's no clear path to trace the source of the error, understand why it occurred, or prevent it from happening again.

eLLMo AI ingests website content and product feeds, then enriches and structures data into a unified catalog-policy layer. This single source of truth ensures AI assistants access consistent, accurate product and policy information without replatforming. By consolidating disparate data sources into one authoritative system, you eliminate contradictions and ensure AI responses reflect the most current information.

Leverage MerchantReturnPolicy and WarrantyPromise schema to explicitly define shipping, returns, and warranty terms. Structured data enables AI to extract precise policy facts, improving answer accuracy and compliance. Schema markup provides machine-readable policy definitions that AI assistants can reliably parse and apply, reducing misinterpretation risks.

Employ Retrieval-Augmented Generation (RAG) techniques to feed AI assistants with fresh, transactional-ready data. eLLMo's approach keeps AI responses current, reducing stale or conflicting answers. RAG ensures AI assistants pull from the latest product catalogs, policy documents, and inventory status rather than relying on outdated training data.

Deploy agentic workflows that allow tracing AI answers back to source data. This transparency empowers support leaders to audit and correct AI misrepresentations proactively. With full data lineage, you can identify when and why AI provided incorrect information, enabling continuous improvement and error prevention.

Accurate AI assistants deflect routine policy queries, lowering support tickets. Precise policy answers reduce refund leakage and chargebacks, protecting revenue and improving operational efficiency. When AI provides correct information the first time, customers get the answers they need without escalation, and finance teams avoid unnecessary refunds.