Aging infrastructure at risk
Defense, aerospace, energy and infrastructure assets are aging faster than inspection capacity. Late detection means unscheduled maintenance, downtime, higher repair cost and safety risk.
Where NDE meets artificial intelligence
Engineering intelligence beyond inspection.
We turn nondestructive evaluation and engineering data into decisions you can defend, using physics-based AI for automated defect detection, characterization and structural integrity assessment.
5+core capability domains
4target industries
R&D → Fielddeployment pipeline
UT Studio · CFRP A-scanClassifying
Signal#76
AI verdict—
Confidence—
Measured pulse-echo signals from our CFRP panels, shown with UT Studio's cross-validated AI verdicts. Demonstration only, not for inspection decisions.
Defense
Aerospace
Energy
Infrastructure
The industry gap
Critical assets are aging faster than inspection can keep up.
Organizations collect more inspection data than ever, but most of it is never fully analyzed. Four gaps keep asset owners reactive.
Data without intelligence
- Massive inspection datasets, rarely fully analyzed
- Critical signals buried in noise
- Siloed data that doesn't communicate
Reactive, not predictive
Most organizations still fix failures after the fact. Predictive maintenance through intelligent analytics can substantially reduce lifecycle costs.
Manual inspection gaps
Traditional NDE is slow, labor-intensive and operator-dependent. Results can be subjective and hard to trace, which is not enough for high-consequence, high-volume work.
Our solution
Engineering intelligence, end to end.
One integrated pipeline from sensor to decision. Select a stage to see what we deliver.
Featured work
Research and tools you can explore.
Three programs from our lab, each with a live demo that runs in your browser using real data or exact reference solutions.
Ultrasonic NDE · AI · UT Studio
Intelligent Ultrasonic Characterization
Ultrasonic testing is the workhorse of nondestructive evaluation. It is used on aerospace structures, welds, pipelines, pressure vessels and civil infrastructure. Interpreting the signals still depends on skilled operators working one A-scan at a time, and results get harder to trust on curved or complex geometry.
DNE built UT Studio, a platform for ultrasonic analysis and automated defect recognition (ADR). It builds A-, B-, C- and D-scans, runs a 21-step signal-processing chain and adapts its gates to curved geometry. It also trains explainable AI models on your own labelled data, so the same workflow carries over to new materials, parts and defect types.
AI accuracy · 5-fold CV98.8%
Curved-part stress test99.5%
Built-in classifiers8
Processing chain21 steps
Results from DNE's composite inspection study (600 measured A-scans, 3 test panels, 5-fold cross-validation). Performance on other materials, geometries and equipment will differ and must be validated for each application.
Case study
Composite Inspection
DNE LLC's UT Studio AI tool labels every A-scan as no defect, defect, and other features, and its adaptive gating is tested on a curved composite skin. The accuracy figures above come from this study.
Specimens & scans
Known defects, seen through UT Studio
Three test standards with embedded defects. Pick one to compare the design drawing with UT Studio's scan of it.
Curved parts · adaptive gating
Real parts are curved. Fixed gates aren't.
Panel R is a simulated curved skin (2 m radius) with a 1.2 mm joggle. With fixed gates, signal loss from curvature looks like damage. UT Studio tracks the front wall of every A-scan and compensates for tilt, so only the real defects remain. Drag the divider to compare.
The blank strip at 162–175 mm is the joggle: the surface is too steep for the echo to return, so UT Studio flags it as geometry-limited.
False calls on sound material20.9%0.2%
Area silently lost7.9%none
Measured thickness (mm)3.40 ± 0.063.360 ± 0.006
4% porosity zone detected21%64%
FixedAdaptive
Areas too steep to inspect (7.3%) are flagged as geometry-limited instead of being called defects. For the AI, training with curved-part augmentation keeps accuracy at 99.5% on geometry-distorted signals. The same model trained only on flat panels drops to 66%.
Try the AI
Ask UT Studio's AI about a real A-scan
Pick a defect type and draw a measured signal. You'll see it against each class's average waveform and the AI's confidence for each class. Each verdict comes from cross-validation, so the model that judged the signal never saw it in training.
1. Choose where the signal came from
2. UT Studio AI confidence
Demonstration only. Results are from specific test specimens or simulations and must not be used for inspection acceptance, certification or safety-critical decisions.
Model benchmark
Seven classifiers, 5-fold cross-validation, 600 measured signals
Select a classifier to see its confusion matrix: rows are the true defect type, columns what the model predicted. Switch to the curved-part stress test to see which models survive geometry distortion.
Overall accuracy (%). The bars start at 50% so small differences are visible.
predicted class →
Demonstration only. Results are from specific test specimens or simulations and must not be used for inspection acceptance, certification or safety-critical decisions.
Inside UT Studio
Post-processing and AI in one workspace
Real screens from UT Studio. Pick a capability, and click the screenshot to see it full size.
Experimental mechanics · Full-field strain
DIC Studio: 2D digital image correlation for research and industry
Digital image correlation (DIC) measures displacement and strain over a whole surface by tracking a speckle pattern between photographs. Strain gauges give one number at one point. DIC shows where strain concentrates around holes, cracks and joints.
DNE built DIC Studio, a desktop DIC application with a native multi-threaded correlation engine. It follows a guided, step-by-step workflow and adds metrology tools that let engineers quantify measurement error on their own speckle pattern.
Engine throughput~100k pts/s
Validation tests (synthetic reference cases)20 / 20 pass
AlgorithmIC-GN, reliability-guided
Strain measuresGreen-Lagrange, log, more
ExportsCSV, .mat, HDF5, PNG, GIF
DataStays on your machine
Interactive · Open-hole tension coupon
Watch strain concentrate around a hole
A plate with a hole is pulled left-to-right. Move the load slider and hover over the specimen to probe strain. The overlay is the exact Kirsch solution that DIC Studio's synthetic test images are generated from, so the software can be checked against a known answer.
Peak εxx at hole edge—
Concentration factorKt = 3.0
Hole diameter7.0 mm
Image scale0.05 mm/px
Demonstration only. Results are from specific test specimens or simulations and must not be used for inspection acceptance, certification or safety-critical decisions.
What DIC Studio adds
Beyond a standard DIC workflow
- Native multi-threaded engine. IC-GN with a shared reliability-guided queue, so one seed per region is enough.
- Robust seeding. Predicted motion, FFT cross-correlation and whole-image search. Unreached regions are re-seeded automatically.
- High-strain mode. Automatic reference updating, with results mapped back to the original grid.
- Metrology tools. Speckle-pattern grading, scale and lens-distortion calibration, noise floor and self-test.
- Material properties. Load force data to compute Young's modulus and Poisson's ratio.
- Non-optical data. Correlate C-scans, X-ray or SEM maps as well as photographs.
Reliability · Inspection qualification
POD Analyzer: how reliably does your inspection find flaws?
Probability of Detection (POD) is the standard way to prove an inspection is good enough. Engineers inspect specimens with known flaws, record hits and misses, and fit a curve. The key number is a90/95: the flaw size found 90% of the time, with 95% confidence.
DNE's POD Analyzer implements hit/miss POD analysis with both parametric (log-logistic) and nonparametric binomial methods. It also supports four-arm studies that compare human inspectors, AI, and the two working together, which matters when you are qualifying AI-assisted inspection.
AnalysisHit/miss POD
MethodsParametric + binomial
Confidence boundLikelihood ratio
Cross-checkParametric bootstrap
Human + AI studies4-arm, GEE
RunsOffline desktop app
Interactive · Hit/miss POD study
Run a virtual POD study
Set how good the "true" inspection is and how many flawed specimens you test. Each run simulates a new study, fits a log-logistic POD curve by maximum likelihood, and computes the 95% lower confidence bound. See how sample size changes the a90/95 you can claim.
Fitted POD
95% lower bound
True POD
Hit
Miss
a50 (fitted)—
a90 (fitted)—
a90/95—
Hits / specimens—
Simulated data for illustration. Qualification studies need a designed specimen set and the full POD Analyzer workflow.
Demonstration only. Results are from specific test specimens or simulations and must not be used for inspection acceptance, certification or safety-critical decisions.
How we work
Our engagement & delivery model
A five-phase path from your problem statement to a sustained, field-ready solution.
01
Discovery
the problem statement
Technical assessment of your inspection challenges, data environment and system requirements.
02
Design
ideation
Physics-based solution architecture: sensor selection, ML pipeline design and modeling approach.
03
Develop
problem solving
Laboratory development and validation of NDE methods, AI models and signal processing algorithms.
04
Deploy
implementation
Field integration, in-situ testing, operator training and performance verification on real assets.
05
Sustain
continuous improvement
Ongoing analytics support, model retraining, lifecycle monitoring and continuous improvement.
Competitive advantage
Why DeepNeuro Engineering?
01
Explainable, physics-grounded AI
Our models use physics-based signal features, and every call comes with the evidence behind it, so results can be checked against how real materials and structures behave.
02
End-to-end capability
From laboratory R&D through field deployment, we cover the full pipeline: sensing, signal processing, AI analysis, modeling and decision support, delivered as one integrated solution.
03
Built for high-consequence environments
Our methods are designed for harsh environments, noisy data and applications where reliability matters most: the standard that defense, aerospace and energy demand.
04
Multi-domain integration
We combine NDE hardware expertise with AI software, experimental mechanics and simulation. Few teams integrate all four, and doing so closes the gap between data and insight.
Stakeholder benefits
What we deliver for you
Target markets
Industries we focus on
Advanced engineering intelligence for systems operating in high-consequence environments, where precision and reliability are non-negotiable.
Defense
- Structural readiness of military assets
- Airframe & hull inspection systems
- Support for certification & sustainment
- Field-deployable autonomous inspection
Mission readiness & force protection
Aerospace
- Support for safety & airworthiness certification
- Composite structure assessment
- Fatigue & fracture analysis
- Reduced MRO cost & turnaround time
Airworthiness support & fleet longevity
Energy
- Pipeline & pressure vessel inspection
- Weld quality & integrity analysis
- Predictive maintenance programs
- Regulatory compliance support
Uptime reliability & risk mitigation
Infrastructure
- Inspection data & analytics for bridges and civil structures
- Early defect detection & monitoring
- Digital twin integration
- Data to support repair prioritization by the engineer of record
Better-informed maintenance decisions
Who we are
A deep-technology NDE and AI company.
DeepNeuro Engineering LLC (DNE) specializes in nondestructive evaluation, experimental mechanics and intelligent data-driven analysis. We integrate physics-based modeling, advanced signal processing and artificial intelligence to assess structural integrity, detect and characterize defects, and predict performance and life.
Our team combines hands-on field testing experience with rigorous research and development, enabling rapid transition from concept to deployment. From laboratory experiments to in-field validation, we support the full lifecycle of engineered systems: design, verification, operation and sustainment.
5+Core capability domains
4Target industries
3In-house software tools
Engineering Intelligence Beyond Inspection
Smarter Diagnostics. Stronger Systems.
From Inspection to Insight
Our mission
Transform NDE and engineering data into actionable insight, delivering reliable, AI-driven solutions for automated defect detection, characterization and structural integrity assessment.
Our vision
A future where intelligent, autonomous inspection systems redefine how components and structures are designed, evaluated and sustained, making critical infrastructure safer, more efficient, more reliable and more resilient.
Let's talk
Let's build something smarter together.
We work with defense contractors, aerospace OEMs, energy operators and infrastructure owners to deploy next-generation NDE and AI solutions.
Based inSacramento, California
Focus industriesDefense · Aerospace · Energy · Infrastructure