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Kaiterra Launches Kaiterra AI and the Kaiterra MCP for Deep Indoor Environmental Quality Analysis

Kaiterra AI answers questions about indoor environmental quality in plain language, drawing on a customer's own monitor readings. Image taken from Kaiterra website

Kaiterra announced Kaiterra AI, an indoor environmental quality assistant built on the Kaiterra Data Platform, and the Kaiterra MCP, a Model Context Protocol connector that brings the same data into a company’s existing AI environment. Both answer questions drawing directly on readings from customers’ own Kaiterra monitors, with no dashboards to navigate, no CSVs to export, and no waiting on a specialist.

“A single monitored floor generates hundreds of thousands of data points a month, and a portfolio generates more than any team can read, let alone act on,” said Liam Bates, CEO of Kaiterra. “That volume is why turning building data into decisions has always been slow, and why it has stayed with specialists. Kaiterra AI puts the data our monitors have already collected into the hands of anyone on a building team, so they can resolve a tenant complaint, confirm an HVAC adjustment worked, or pull evidence for a certification review in minutes.”

Image credits: prnewswire

Analysis across a portfolio, with every source named

Kaiterra AI runs structured analysis across single buildings and portfolios, covering air quality, thermal comfort, occupancy, sensor network health, and every other parameter a customer’s Kaiterra monitors measure. It investigates alerts against occupancy, outdoor air, and HVAC context rather than reporting them in isolation, and it understands how WELL, LEED, RESET, and ASHRAE define performance. Each answer cites the tool that produced the figure, the buildings it covers, and the period it draws on, so any number traces back to the readings.

Built for enterprise governance

Kaiterra AI sees exactly what the user sees, nothing more. Every query is authenticated with the user’s credentials and governed by the same role-based permissions as the platform. Customer-identifying data is pseudonymized server-side before reaching the model, no customer data trains third-party AI models, and zero-data-retention terms apply with Kaiterra’s model provider. Organizations can opt out of AI processing.

For customers whose AI policies require internally approved tools, the Kaiterra MCP connects the same data and analytics layer to the AI environment they already run.

Availability

Customers can request access at the official website of Kaiterra.

Source

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