Service

Sensor Fusion & Signal Intelligence

Multi-modal sensor data fusion for robust perception, intelligence extraction, and situational awareness in complex operational and industrial environments.

Energy & Utilities
Power Grid Resilience • Physics-Informed AI • Critical Infrastructure

America's largest grid operator hit its first-ever capacity shortfall: 6,623 MW. The $16.4B auction maxed out FERC's price cap. Texas has 233 GW stuck in queue. ⚡

6.6 GW
PJM capacity auction shortfall threatening grid reliability for 2027/2028
PJM Interconnection Capacity Auction
87x
Faster stability analysis with Physics-Informed Neural Networks vs conventional solvers
PINN Benchmark Study
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The Sentinel Grid

PJM's first-ever 6,623 MW capacity shortfall and ERCOT's 233 GW interconnection backlog expose a grid reliability crisis that legacy control systems cannot solve without physics-informed AI.

GRID CAPACITY CRISIS LOOMS

North American electrical infrastructure has entered structural instability. PJM retired 54.2 GW of thermal capacity while ERCOT faces a 233 GW interconnection queue on an 85 GW grid. Data center demand surges up to 6.4% annually in critical zones.

DEEP AI SENTINEL GRID
  • Physics-informed neural networks embedding swing equations directly into loss functions for real-time solving
  • Graph neural networks mapping grid topology to predict cascade propagation in milliseconds
  • Reinforcement learning agents optimizing dispatch via constrained Markov decision processes
  • Dynamic line rating with AI-driven atmospheric modeling unlocking 20-40% additional transmission capacity
PINNsGraph Neural NetworksReinforcement LearningDynamic Line RatingEdge AI
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Data Center Grid Impact • Physics-Constrained AI • Hyperscale Operations

One lightning strike in Virginia triggered 60 data centers to disconnect simultaneously — shedding 1,500 MW (Boston's entire power consumption) in 82 seconds. ⚡

1,500 MW
Instantaneous load loss when 60 data centers shed demand in 82 seconds
NERC Virginia Grid Disturbance Report
0.64 MW
PINN prediction deviation outperforming standard neural networks in grid forecasting
PINN Grid Performance Benchmark
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Structural Resilience & Physics-Constrained Intelligence

A single lightning strike caused 60 data centers to simultaneously shed 1,500 MW — 50x faster than a typical plant failure — exposing the systemic grid risk of hyperscale computing clusters.

DATA CENTERS THREATEN GRID STABILITY

A routine lightning strike triggered cascading UPS disconnections across 60 Virginia data centers. Each voltage dip was individually within tolerance, but cumulative counting logic shed 1,500 MW of demand in 82 seconds, requiring unprecedented reverse stabilization.

PHYSICS-CONSTRAINED GRID INTELLIGENCE
  • Physics-informed neural networks providing sub-millisecond grid-forming control with 0.64 MW prediction accuracy
  • Neuro-symbolic sandwich architecture ensuring grid operations comply with Kirchhoff's laws deterministically
  • Bottom-up demand forecasting from IT hardware and cooling specs replacing speculative growth projections
  • Coordinated reconnection orchestration preventing the manual intervention bottleneck after cascade events
PINNsNeuro-Symbolic AIGrid-Forming ControlSensor FusionNERC Compliance
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Automotive
Autonomous Vehicles • Safety-Critical AI • Formal Verification

Uber's self-driving AI reclassified a pedestrian 6 times in 5.6 seconds — resetting her trajectory each time. It realized it needed to brake 1.3 seconds before impact. Physics said no. 🚗

$8.5M
Uber ATG settlement after fatal pedestrian crash caused by perception failure
NHTSA Investigation Report
40+
Active NHTSA investigations into Tesla FSD across 2.9M vehicles
NHTSA PE25-012
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From Stochastic Models to Deterministic Assurance

Autonomous vehicle AI systems reclassify objects mid-trajectory, resetting predictions each cycle. Without formal verification, probabilistic models create fatal blind spots in safety-critical decisions.

STOCHASTIC MODELS KILL SAFETY

Autonomous vehicles built on probabilistic AI suffer from classification oscillation, post-impact blindness, and sensor saturation. The gap between what AI perceives and what it should logically conclude has caused fatal incidents across Uber, Cruise, Tesla, and Waymo deployments.

DETERMINISTIC ASSURANCE ENGINEERING
  • Bird's-eye-view occupancy networks that track volume, not labels, eliminating classification oscillation
  • Formal verification with mathematical proofs ensuring safety-critical decisions meet deterministic thresholds
  • Sensor fusion combining LiDAR, radar, and vision with spatiotemporal consistency across occlusions
  • Assurance Gate architecture that transitions to minimal risk condition based on proof, not probability
Formal VerificationOccupancy NetworksSensor FusionBEVFormerPhysics-Constrained AI
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Insurance & Risk Management
Remote Sensing, Satellite AI & Enterprise Intelligence

A logistics conglomerate's AI flagged a highway as 'Flooded.' 50 trucks diverted 100km. Cost: $250,000+. Reality? A cumulus cloud cast a shadow. Single-frame AI hallucinates shadows as floods. ☁️

85%
False Positive Reduction (Shadow Confusion vs Static Baseline)
Veriprajna Chronos-Fusion Benchmarks 2024
0.91
mIoU Accuracy Score (Spatio-Temporal Fusion)
Veriprajna Performance Benchmarks 2024
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The Shadow is Not the Water: Beyond Single-Frame Inference in Enterprise Flood Intelligence

Veriprajna's spatio-temporal AI solves false positive flood detection by distinguishing cloud shadows from actual floods using Optical-SAR fusion and 3D CNNs.

SINGLE-FRAME AI FAILURES

Single-frame AI confuses cloud shadows with floods. Lacks temporal context and physics understanding. False positives cost $250K+ per logistics incident through unnecessary rerouting.

SPATIO-TEMPORAL FUSION ARCHITECTURE
  • 3D CNNs capture temporal motion patterns
Spatio-Temporal AI3D CNNSAR-Optical FusionConvLSTM
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Healthcare & Life Sciences
Ambient Assisted Living, Healthcare IoT & Elder Care

Wearables fail when needed most: 30% abandonment within 6 months, removed during showers (highest fall risk), forgotten by dementia patients. Passive Wi-Fi Sensing transforms existing networks into invisible guardians—99% fall/respiratory detection accuracy with zero user compliance required.

30%
Wearable Abandonment Rate
Monitoring Studies 2024
99%
Passive Detection Rate
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The Invisible Guardian: Transcending Wearables with Passive Wi-Fi Sensing and Deep AI

Wearables have 30% abandonment, removed during showers, forgotten by dementia patients. Veriprajna's Passive Wi-Fi Sensing analyzes CSI from existing infrastructure achieving 99% fall and respiratory detection accuracy with zero user compliance required.

COMPLIANCE CRISIS

Shower Paradox: bathroom most hazardous yet devices removed. Charging fatigue: 24% never wore pendants. Stigma of frailty: devices hidden in drawers. Compliance gap creates perilous chasm between theoretical safety and practical reality.

PASSIVE WI-FI SENSING
  • CSI captures per-subcarrier amplitude and phase enabling breathing detection accuracy
  • Dual-Branch Transformers with DANN achieve environment-invariant features under 300ms latency
  • Three modalities: respiratory monitoring fall detection sleep quality with zero compliance
  • IEEE 802.11bf standardization enables zero-hardware retrofit via software update
wifi-sensingchannel-state-informationpassive-monitoringdual-branch-transformer
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Sports, Fitness & Wellness
Computer Vision, Sports Technology & Enterprise AI

An AI-powered soccer camera mistook a bald linesman's head for the ball, panning away from the goal. Generic CV sees textures—Veriprajna embeds physics.

99.99%
Physics-Constrained Accuracy
Veriprajna Systems 2024
<300ms
Real-time FPGA Latency
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Beyond the Bounding Box: The Imperative for Physics-Constrained Intelligence in Enterprise AI

Soccer camera mistook bald head for ball—visual probability ignored physical impossibility. Veriprajna embeds physics constraints (kinematics, optical flow, PINNs) into vision systems, achieving 99.99% accuracy versus 90% generic APIs.

GENERIC VISION FAILS

Generic APIs detect patterns without understanding physics. No temporal consistency, no object permanence. Visual similarity conflicts with physical impossibility in dynamic environments. Business risk lives in last 10%.

PHYSICS-CONSTRAINED VISION
  • Kalman Filters maintain probabilistic object state predictions with kinematic gates
  • Optical Flow validates velocity constraints rejecting physically impossible detections
  • PINNs encode differential equations into loss functions for physical laws
  • FPGA deterministic verification achieves under 300ms latency with physics gates
Physics-Constrained AIKalman FiltersComputer VisionOptical Flow
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Sports Technology, Football Officiating & Sensor Fusion

Current VAR makes definitive offside calls with a 28-40cm margin of error—larger than the infractions judged. Veriprajna reduces uncertainty to 2-3cm with 200fps cameras + 500Hz ball IMU.

28cm
Current VAR Error
50fps Systems 2024
2-3cm
Veriprajna Precision
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The Geometry of Truth: Re-Engineering Football Officiating Through Deep Sensor Fusion

Current VAR has 28-40cm error—larger than infractions judged. Veriprajna achieves 2-3cm precision using 200fps global shutter cameras, 500Hz ball IMU, skeletal tracking network, and Unscented Kalman Filter fusion for physics measurement.

PIXEL FALLACY

50fps creates 20ms gaps. Player at 10m/s travels 20cm between frames. Motion blur and frame lottery mean operators guess position within 30cm uncertainty zone. Definitive calls lack physical capture.

DEEP SENSOR FUSION
  • 200fps global shutter cameras eliminate rolling shutter distortion
  • 500Hz ball IMU detects kick to 1ms precision solving frame lottery
  • Skeletal network trained on offside-critical joint points achieves 2cm accuracy
  • Unscented Kalman Filter fuses sensors reconstructing virtual frames under 5s
Deep Sensor FusionUnscented Kalman FilterIMU TrackingGlobal Shutter
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HR & Talent Technology
Enterprise HR & Talent Technology

A Deaf Indigenous woman was told to 'practice active listening' by an AI hiring tool. The ACLU filed a complaint. 🚫

78%
Word Error Rate for Deaf speakers in standard ASR systems
arXiv ASR Feasibility Study
< 80%
Four-Fifths Rule threshold triggering disparate impact liability
EEOC Title VII Guidance
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The Algorithmic Accountability Mandate

AI hiring platforms built on commodity LLM wrappers systematically exclude candidates with disabilities and non-standard speech patterns, turning algorithmic bias into active discrimination.

BIASED BY DESIGN

Standard ASR systems trained on hearing-centric datasets produce catastrophic 78% error rates for Deaf speakers. When an AI hiring tool analyzes such a transcript, its 'leadership trait' scores are hallucinated from garbage data -- yet enterprises treat these outputs as objective assessments.

ENGINEERED FAIRNESS
  • Deploy adversarial debiasing networks penalizing until protected attributes become undetectable
  • Integrate early multimodal fusion with Modality Fusion Collaborative De-biasing
  • Trigger event-driven Human-in-the-Loop routing when ASR confidence drops below threshold
  • Quantify feature attribution via SHAP with continuous Four-Fifths Rule monitoring
Adversarial DebiasingMultimodal FusionSHAP ExplainabilityHuman-in-the-LoopASR Calibration
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Aerospace & Defense
AI Security • Adversarial Defense • Multi-Spectral Sensing

$5 sticker defeats $Million AI system. Tank classified as school bus. 99% attack success. Cognitive armor needed. ⚠️

$5
Adversarial attack cost
DARPA GARD Program
<1%
Multi-spectral attack success rate
Veriprajna Whitepaper
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Cognitive Armor: Engineering Robustness in the Age of Adversarial AI

$5 adversarial stickers defeat million-dollar AI systems with 99% success. Multi-Spectral Sensor Fusion combines RGB, Thermal, LiDAR, Radar reducing attack success below 1%.

AI VULNERABILITY ASYMMETRY

Single-sensor AI systems vulnerable to $5 adversarial stickers. 99% attack success on RGB-only systems. CNNs prioritize texture over shape, creating 1,000:1 cost asymmetry favoring attackers.

MULTI-SPECTRAL FUSION
  • RGB, Thermal, LiDAR, Radar verify truth
  • Thermal sensor detects heat signature anomalies
  • Deep Fusion attention weights sensor reliability
  • NIST AI RMF framework ensures governance
Multi-Spectral Sensor FusionAdversarial DefenseThermal LWIRLiDARRadarDeepMTD ProtocolNIST AI RMFCognitive ArmorPhysics-Based Verification
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Defense & Autonomous Systems • Navigation Technology • Robotics

GPS jamming turns $Million drones into 'paperweights.' VIO navigation: 0ms jamming vulnerability. Un-tethered autonomy. ✈️

$1.4T
GPS economic value generated
NIST Study
0ms
VIO jamming vulnerability
Veriprajna Whitepaper
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The Autonomy Paradox: Engineering Resilient Navigation in GNSS-Denied Environments

GPS-dependent drones fail when jammed. Visual Inertial Odometry enables autonomous navigation without GPS, achieving 1-2% drift rate with zero jamming vulnerability via passive sensing.

GPS FRAGILITY CRISIS

GPS satellites transmit from 20,200km with low power. Ground jammers at 10-40 watts create blackout zones. GPS denial costs US economy $1B daily.

VIO AUTONOMOUS NAVIGATION
  • Visual and inertial fusion achieves autonomy
  • Semantic SLAM understands environment contextually
  • NVIDIA Jetson Orin enables edge AI
  • Defense, mining, infrastructure applications enabled
Visual Inertial OdometryVIOSemantic SLAMEdge AINVIDIA Jetson OrinGNSS-Denied NavigationTensorRTORB-SLAM3Loop Closure
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Media & Entertainment
Enterprise AI Audio & Legal Compliance

Black Box AI audio = ticking legal time bomb. RIAA sues Suno/Udio for massive copyright infringement. $150K statutory damages per work. 🚨

0%
Copyright Risk with SSLE Architecture
Veriprajna SSLE architecture Whitepaper
$150K
Statutory Damages Per Work Infringement
US Copyright Law USC § 504
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The Sovereign Audio Architecture: From Black Box Liability to White Box Compliance

Black Box AI audio trained on scraped data creates $150K statutory damages risk. White Box transformation uses Deep Source Separation and licensed voice actors achieving 0% copyright risk.

BLACK BOX LIABILITY

Models trained on scraped YouTube/Spotify inherit 'poisoned tree' creating direct and derivative infringement. Pure AI works lack authorship, making output uncopyrightable and unprotected from competitors.

WHITE BOX SSLE
  • Deep Source Separation isolates stems deterministically
  • RVC transforms voice using licensed actors only
  • C2PA embeds cryptographic provenance per file
  • Five-phase pipeline ensures verifiable licensing chain
Deep Source SeparationRVCC2PAAudio ProvenanceHuBERTFAISSHiFi-GANDemucsMDX-NetVoice ConversionSSLEU-Net
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Audio Security & Music Industry

$3B annual streaming fraud. 100K tracks uploaded daily to Spotify. 75M+ spam tracks purged. AI-generated 'slop' floods royalty pools. 📊

$3B
Annual Streaming Fraud Loss
Music industry fraud analysis 2024-2025
99%
Watermark Detection Rate
Veriprajna watermarking implementation Whitepaper
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The Unverified Signal: Latent Audio Watermarking in the Age of Generative Noise

$3B annual streaming fraud as AI-generated 'slop' floods royalty pools. Latent Audio Watermarking embeds imperceptible signals surviving Analog Gap achieving 99% detection rate via autocorrelation.

FINGERPRINTING FAILS AI

Fingerprinting fails on new AI-generated tracks having no database match. Analog Gap destroys watermarks through multipath propagation, frequency filtering, and harmonic distortion during speaker-to-microphone transmission.

LATENT AUDIO WATERMARKING
  • Spread Spectrum embeds across entire frequency band
  • Autocorrelation survives Analog Gap via self-comparison
  • C2PA soft binding links watermark to provenance
  • Watermarking recovers $6.5M annually combating fraud
Audio WatermarkingDSSSSVDC2PAAutocorrelationAnalog GapDeepfake DetectionAWARE ProtocolSpread SpectrumPsychoacoustic MaskingFraud PreventionMusic Industry
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Industrial Manufacturing
Manufacturing & Industrial Automation • Edge AI

Your cloud AI is too slow for the factory floor. Defects escape. $39.6M/year lost. 🏭

800ms → 12ms
Latency reduction achieved
Cloud API vs Edge AI
$22K/min
Unplanned downtime cost
Automotive Industry
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The Latency Kill-Switch

Cloud AI latency allows defects to escape ejector. Edge-Native AI reduces latency from 800ms to 12ms, restoring factory floor control.

THE LATENCY GAP

Cloud latency reaches 990ms, exceeding 500ms time budget. Defective parts escape past ejector, costing $39.6M annually in unplanned downtime and losses.

EDGE-NATIVE AI
  • NVIDIA Jetson provides 275 TOPS inference
  • TensorRT optimizes models for 12ms latency
  • Acoustic AI detects bearing failures early
  • Data stays on-device ensuring complete sovereignty
Edge AINVIDIA JetsonTensorRTTinyML Acoustic AIIndustrial AutomationPredictive Maintenance
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Veriprajna Deep Tech Consultancy specializes in building safety-critical AI systems for healthcare, finance, and regulatory domains. Our architectures are validated against established protocols with comprehensive compliance documentation.