Most spatial computing companies build platforms. We built an API. Here's why.
The Platform Trap
Traditional GIS and spatial computing tools are monolithic platforms. They do everything — data ingestion, processing, visualization, analysis — inside a single application. This creates two problems:
- Integration friction — getting data in and out requires export/import workflows
- Innovation bottleneck — new capabilities require the platform vendor to build them
API-First Design
Percept is an API with a platform on top, not the other way around. Every feature available in our UI is available through the API with the same performance and accuracy.
import percept
# The same capabilities, accessible programmatically
client = percept.Client(api_key="pk_live_...")
# Reconstruct
scene = client.reconstruct(source="capture.mp4")
# Query
defects = scene.query(type="defect", severity="critical")
# Simulate
result = scene.simulate(scenario="fire_spread", duration=4)
# Export
scene.export(format="gltf", path="output.glb")Enabling the Ecosystem
An API-first approach means anyone can build spatial AI applications:
- Drone companies integrate reconstruction directly into their flight software
- Insurance platforms automate property assessment with scene queries
- Emergency management systems embed simulation results in their dashboards
- Robotics companies use our scene graphs for autonomous navigation
The most impactful spatial AI applications won't come from us — they'll come from the developers building on top of our API.
Performance at API Scale
API-first doesn't mean slow. Our reconstruction pipeline processes a standard drone capture in under 2 minutes. Scene queries return in milliseconds. Simulations stream results as they compute.
This is spatial intelligence that fits into existing workflows, not a new workflow you have to adopt.