Case Study: Plantd cuts telemetry analysis from weeks to hours with Sift MCP

Plantd saves dozens of hours a week with Sift MCP

Plantd, a company building carbon-negative building materials, faced a significant challenge in analyzing production telemetry data. Their process engineer, Matthew Barr, spent weeks manually exporting data from their Sift monitoring system to CSV files, cleaning it, and pasting it into an AI agent for analysis. This lengthy process of pulling, aligning, and plotting data for each test run was a major bottleneck, taking approximately 40 hours per investigation.

By implementing Sift MCP (Model Context Protocol), the vendor Sift provided a solution that integrated directly with the engineer's existing AI agent. This allowed the agent to speak directly to the Sift platform, discovering assets, fetching channel data, and running analyses without any manual export steps. The results were dramatic: investigation time was reduced from one week to just three hours. This saved dozens of hours per week, enabling the engineer to ask more questions and achieve better answers, not just faster ones.

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