The problem
Every building names its sensors and equipment differently. Building A calls itAHU1_SAT. Building B calls the same thing Supply_Air_Temp_AHU_01. Building C calls it SA-T-1. Three different strings, one physical reality: a temperature sensor on the supply side of an air handling unit.
This means every application, every dashboard, every analytics pipeline is custom-built for each building. Write it once, deploy it to 10,000 buildings? Impossible, until now.
How Tacit solves it
Tacit normalizes building data using the Brick Schema ontology, then exposes it through a modern API that preserves three capabilities no other platform offers:- Class hierarchy inference: query
HVAC_Equipmentand automatically match AHUs, VAVs, chillers, and every other subtype - Transitive traversal: follow equipment chains (chiller → AHU → VAV → zone) without knowing the depth
- Pattern matching: find things by relationship shape, not by ID
Get started
Get your API key and run your first query in under 5 minutes.
Two API surfaces, one platform
Tacit uses GraphQL for reads and REST for writes, the right tool for each job.GraphQL API
Composable, self-documenting queries with semantic resolution. Ask complex questions in a single request.
REST API
Query historical sensor data with the timeseries REST endpoint.
Understand the concepts
How Tacit works
Architecture overview: how buildings connect to your applications through Tacit.
Brick Schema
The universal translator for building data. Understand why this changes everything.
Buildings and points
The core data model: buildings contain equipment, equipment has points.
Relationships & traversal
How equipment connects through feeds, composition, and spatial hierarchy.
Learn by doing
Query your first building
A guided walkthrough with progressively deeper query patterns.
Find equipment by type
Use class hierarchy inference to find equipment without listing every subtype.