Distinguish observation from inference.
Retain source observations and identify inferred geometry. Plausibility alone does not establish a measurement.
World models.
Grounded in
evidence.
WaveForm is an AI-native geospatial runtime. It gives models and tools one time-aware reference frame for phone, drone, satellite and fixed-camera evidence, so they can inspect, reconstruct and compare real places with provenance and uncertainty attached.
Closed beta. Access by invitation.
One reference frame.
Model conceptAerial views · site coverage
ObservedEstimatedUnknown
An initial site state. Unobserved areas remain unknown.
The runtimeGive AI a
Give AI a
spatial contract.
Models and tools should see the same frame, site history and evidence, with observed, inferred and predicted states kept distinct.
How the platform worksTrack physical change.
Separate changes to a place from new coverage, revised alignment or improved reconstruction.
Make measurements testable.
Our metrological goals require calibration, independent reference checks and evaluated uncertainty for a defined use.
The platformOne runtime.
One runtime.
Four connected components.
WaveForm Mobile
Capture field observations with timing, calibration and source context.
Fisher-Floor
Estimate geometry and analyze uncertainty.
WaveForm
Align captures, reconstruct scenes and analyze change.
Polaris Globe
Connect detailed site models to geographic context.
Imagery first. Jobsite, traffic and field cameras—and other in situ sensors—extend the platform’s scope.
For program and operations teams, see the Field Programs brief .
Local controlLearn across sites.
Learn across sites.
Keep the evidence local.
We’re designing cross-site learning around explicit permission while retaining each site’s observations and history.
Local-first and local-only deployment paths put data and processing under customer control. Each beta build defines the available options.
CLOSED BETA
Try WaveForm.
The first beta focuses on supported LiDAR-equipped iPhones and Apple-silicon Macs for capture and local reconstruction. Tell us what you capture and which devices you use.
Join the waitlist
Access is by invitation.
We’ll contact you when there’s a fit.
The team
Built from field experience.
WaveForm grew out of our own work developing and operating large energy projects, where site evidence has to hold up long after the crew leaves. The platform combines technical leadership from Wesley Ladd with infrastructure and government experience from Saxon Metzger.
Technical leadership
Wesley Ladd
Chief Technology Officer
Wesley leads architecture and engineering with three developers. He has 10 years of cybersecurity experience and several years in computer vision and AI.
He studied philosophy, holds an MBA and coauthored Practical AI for Professionals, published by Chapman & Hall / Taylor & Francis.
Infrastructure & government
Saxon Metzger
Chief Executive Officer, field infrastructure practice
Saxon brings experience across energy infrastructure, government planning, inspection and auditing, including project development, permitting, procurement and asset lifecycle management.
He serves on state and federal advisory bodies and is a professor of economics, business and finance at Wilmington University.
Details
Scope and limits.
What does the beta support?
Each build defines its supported devices, workflows and limitations. The broader platform direction includes more sensors and deployment options than the initial beta.
What are the metrological goals?
Measurement results supported by calibration, independent evaluation and stated uncertainty. These properties must be validated for specific workflows and uses; visual completeness alone does not establish accuracy.
Will my captures train a shared model?
Shared learning requires explicit permission. Source observations do not automatically become a shared training dataset.