In development
One pipeline, from raw data to a decision you can defend.
The Sondera platform moves inspection data from ingestion through physics-grounded signal processing and detection models to traceable, audit-ready reports. Here is how it works.
How it works
Four stages from signal to decision.
Each stage produces a defined output that feeds the next, so the path from a raw scan to a signed-off report is explicit end to end.
01
Ingest
Upload scans in their native formats. Datasets are organized by part, campaign, and modality, and versioned so every later result traces back to an exact input.
OutputVersioned dataset
02
Signal processing
Denoising, deconvolution, alignment, and feature extraction tuned to the physics of the modality. Raw measurements become clean, comparable, decision-ready data.
OutputConditioned data
03
Detection and characterization
Physics-informed models locate indications, estimate size and depth, and classify defect types. Every call carries a confidence measure.
OutputDefect map + confidence
04
Decision and reporting
Results map to your acceptance criteria and produce a traceable, audit-ready report that links each call back to the raw data and the model version behind it.
OutputSigned-off report
Architecture
One core, every modality.
Sondera is built in layers. Modality adapters normalize incoming data so the signal-processing core, the detection models, and the reporting layer never have to care which instrument produced it. Terahertz runs on this core today; ultrasonic and micro-CT reuse the same pipeline as they come online.
A data and audit store sits under all of it, versioning inputs, results, and the model that produced them.
Modality adapters
Bring THz, ultrasonic, or micro-CT data into a common representation.
Signal-processing core
Shared, physics-grounded conditioning and feature extraction.
Detection models
Physics-informed ML for localization, sizing, and classification.
Decision and reporting
Acceptance criteria applied, reports generated.
Data and audit store
Versioned inputs, results, and model versions, kept for audit.
What it produces
Every run leaves an auditable trail.
Defect maps
Spatial maps of detected indications, registered to the part and comparable across campaigns.
Characterization with confidence
Size, depth, and type per indication, each reported with a confidence measure rather than a bare label.
Audit-ready reports
Structured reports that state the acceptance criterion applied and link every call to its evidence.
Versioned datasets
Inputs, results, and the model version that produced them, retained so any decision can be reproduced.
Traceable by default
Every decision links back through a fixed chain, so an audit is a lookup rather than an investigation.
The platform is being built with pilot partners.
We are onboarding a small group working with composite materials and terahertz data. Pilot partners get first access and shape the roadmap.