High-quality content is critical for driving customer satisfaction and conversions across digital platforms and e-commerce. Ensuring that essential information is complete, accurate, and aligned with customer expectations presents a significant challenge at scale. Existing approaches to content evaluation often treat all information uniformly, without prioritizing based on customer relevance, and rely heavily on manual prompt design to encode domain expertise into Large Language Models (LLMs). We present ISEE, a unified framework that addresses these limitations through three core innovations: (1) automated identification of customer-impacting features by synthesizing signals from search behavior, queries, and feedback, enabling targeted content improvements; (2) an instruction-tuned multimodal LLM trained to reliably follow structured operational guidelines, reducing dependence on manual prompt engineering; and (3) robust zero-shot generalization to new product content, features and SOPs via targeted instruction tuning. Evaluated across 20 product categories and 150 product specific features, ISEE achieves 90% precision at 78% recall in detecting content inconsistencies, outperforming much larger (> 200B parameters) models while using a compact 12B architecture.