Does BAAP depend on perfect data?
No. In practice many buildings start with partial consistency. BAAP treats data quality as part of the job and helps expose where trust in the signal is lower.

This page covers the questions teams usually ask before deciding whether BAAP fits the building, the process, and the technical reality on site.
BAAP stands for Building As A Person. It is the layer that helps a building describe what is happening through telemetry, context, and technical signals, then turn that understanding into action.
BAAP Telemetry is the team behind BAAP. We build the data, AI, and operational model that makes building signals easier to understand, prioritize, and act on.
BAAP is for owners, operators, facility teams, technical managers, and service partners responsible for how a building performs, consumes, alarms, and gets serviced.
No. BAAP sits above the systems already in place. We work with available telemetry, metering, BMS/HVAC signals, logs, and documentation rather than starting with a replacement project.
No. BAAP starts with buildings, but the same logic of signals, deviations, actions, and KPIs also applies to factories, agriculture, processing, and other repeatable operational environments.
The usual first step is a focused pilot on your own data. We review what is already available, identify patterns and blind spots, and summarize the priorities and next rollout options.
BAAP groups signals into readable incidents, adds building and document context, and prepares a problem description that service teams can use immediately.
Yes. Deployment depends on the building, the data sources, and security requirements. We can work in cloud, hybrid, or local setups where the project needs them.
No. In practice many buildings start with partial consistency. BAAP treats data quality as part of the job and helps expose where trust in the signal is lower.
No. The same operating model can be used on one asset or across a portfolio, as long as the data structure is mapped clearly enough.
It should not be. Recommendations need traceable data context, understandable reasoning, and a clear path to review, approval, and measured effect.
We can look at the actual building context rather than continue at the level of generic assumptions.