Pre-revenue · ready to pilotOpen demos — no login wall. Authenticated pilots at gprkinetic.pro/os.

Target ballast spendby chainage. Not corridor.

Kinetic GPR ingests GPR surveys through Theorem — our inference core — and returns chainage-locked fouling maps and Selig Fouling Index (FI) proxies. Track engineers sign off on clean, fouled, and marginal ballast calls; asset managers get a maintenance scope they can put in front of procurement.

Diagnostic twin
Open →

Economic Oracle

Compare blanket vs targeted CapEx

Fouling and moisture cluster by chainage. Most corridors do not need uniform ballast replacement. Adjust length and unit cost below; defaults follow the GTA commuter reference case from public GPR literature.

km
$M

Blanket renewal

$18.0M

100% corridor replacement assumption

Targeted intervention

$7.0M

~39% of chainage · GTA reference case

Scenario capital differential

$11.0M

GTA reference case · your inputs recalculate live

Request pilot briefing
Pre-revenue · ready to pilot

Theorem

Subsurface certainty. Delivered.

Theorem is the inference and ontology layer behind Kinetic GPR — the neocortex that turns radargrams into chainage-locked ballast calls your track engineers can sign off on.

Stop interpreting in isolation. Start knowing by chainage — with a semantic rail model built for pilot corridors today and production ontology workflows next.

  • Scan ingest

    Vendor-native GPR (.DZT, SEG-Y) aligned to chainage

  • Semantic ontology

    Track, corridor segment, and ballast objects — not flat CSV rows

  • Theorem inference

    Res-SAM segmentation with engineer sign-off before commit

  • Proof surface

    Diagnostic twin and CapEx scenarios asset managers can defend

Theorem · inference core
Res-SAM · ontology

Ingestion pipeline

Raw acquisition to verified anomaly

Upload vendor-native radargrams (.dzt, .rd3). The pipeline applies dewow, gain, and depth conversion, then runs Res-SAM segmentation with engineer sign-off before anomalies sync to the diagnostic twin.

Ingestion

Illustrative demo — the live pipeline runs in authenticated pilots.

Drag and drop zone

Upload raw radargrams (.dzt, .rd3)

B-scan review

Scan: LBK-1004
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Research stack

AI model architecture

Production path: Res-SAM segmentation on B-scans plus structured regressors for Selig FI and permittivity. Legacy CRNN/XGBoost research metrics below are validation benchmarks on annotated corridor sets — not deployed edge models.

Spatial · research

CRNN on B-scans

Hyperbola and layer-boundary patterns in radargrams — benchmark precision on held-out annotations.

Research val precision (not deployed)83.4%

Tabular · research

XGBoost on A-scan features

Amplitude, phase, and attenuation vectors for fouling index and moisture estimation.

Val accuracy86.0%

Origin

Built by a track inspector. Scaled by ML engineers.

Kinetic GPR wasn't built in a lab. It was built by a former CN track inspector who saw how much ballast money was burned on blanket renewals without subsurface data.

Backed by Palantir's Founder Foundry

Pilot program

Request a corridor briefing

Share your corridor and role — we respond with a scoped demo and deployment outline. Open dedicated briefing page →

Prefer one-step access? Register with LinkedIn for the pilot hub.

Strategy Copilot

Gemini 2.5 Flash

🤖
Kinetic AI initialized. I am your strategic copilot for subgrade analysis and trackbed health.
System ready. Awaiting scan selection.