AI is no longer just about intelligence— it’s now about understanding your business’s unique context. LLMs like OpenAI’s GPT-4 and Google’s Gemini lack built-in access to external data sources. If you ask them to verify the sum of orders in an SQLite database and match it with a PowerPoint presentation, the model alone can’t do it — it needs access to those files and databases.
This is where the Model Context Protocol (MCP) comes in. It bridges the gap by providing them with the necessary access to external data sources, such as databases and file systems. MCP allows them to interact with servers that provide tools, resources, and reflection capabilities, helping them handle complex queries.
Amit Kumar Jena | Lead – AI/ML
Amit leads the AI team at Kanerika, where he develops practical strategies to help organizations implement AI solutions and maximize the value of their data assets. With extensive experience in Python development, Amit specializes in statistical modeling, machine learning, and natural language processing. His technical expertise includes data preparation methodologies, predictive analytics, and advanced regression techniques. Amit’s approach combines technical depth with business understanding, enabling him to translate complex AI concepts into measurable outcomes for enterprise clients.
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