Service

Model Development & Fine-Tuning

Custom AI model development and foundation model fine-tuning for domain-specific tasks, specialized applications, and industry-specific use case requirements.

Transport, Logistics & Supply Chain
Logistics & Operations Research

$1.2B lost. 7 days. 16,900 flights canceled. Crews stranded, 8-hour hold times. Legacy solver optimized phantom airline. Combinatorial cliff. ✈️

$1.2B
Southwest Airlines Loss (7 days)
DOT investigation Southwest filings
66%
Cancellation Reduction (GRL)
Veriprajna simulation Whitepaper
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The Computational Imperative: Deep AI, Graph Reinforcement Learning, and the Architecture of Antifragile Logistics

Southwest's 16,900 flight cancellations cost $1.2B over 7 days. Legacy solvers hit computational cliff. Graph Reinforcement Learning achieves 66% cancellation reduction via topology-aware optimization.

LEGACY SOLVER FAILURE

Southwest canceled 16,900 flights over 7 days. Legacy solvers hit computational cliff with stale data. Point-to-Point topology created cascading failures no system could manage.

GRAPH REINFORCEMENT LEARNING
  • GNN message passing provides topology awareness
  • Multi-agent RL learns strategic sacrifice policies
  • Digital Twins simulate years of operations
  • Action masking ensures constraint compliance always
Graph Reinforcement LearningGraph Neural NetworksGraph Attention NetworksReinforcement LearningMulti-Agent RLProximal Policy OptimizationDigital TwinsNeuro-Symbolic AIAction MaskingSet PartitioningColumn GenerationOperations ResearchCrew SchedulingFleet OptimizationAntifragile Systems
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Retail & Consumer
QSR Voice AI • Edge Inference • Inclusive ASR

Wendy's drive-thru AI makes customers repeat orders 3+ times and is 'unusable' for 80 million people who stutter. They're expanding to 600 locations anyway. 🍔

14%
Order failure rate requiring human rescue in current drive-thru voice AI systems
QSR Drive-Thru AI Performance Study
<300ms
Gold standard latency threshold for natural voice interaction in drive-thru
Voice AI Latency Benchmark
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Beyond API Wrappers in Voice AI

Drive-thru voice AI systems with 14% failure rates cut off customers mid-sentence and exclude 80+ million people who stutter — optimizing for upsell metrics while ignoring the human toll.

VOICE AI EXCLUDES MILLIONS

Drive-thru accounts for 75-80% of QSR sales yet current AI deployments cause 3x repeat attempts. Stuttering affects 80 million people globally and current ASR models return negative BERTScores on disordered speech, creating systemic exclusion.

INCLUSIVE EDGE VOICE ARCHITECTURE
  • Multi-layered neural VAD with probability scoring and context-aware turn-taking replacing binary thresholds
  • Disfluency-aware ASR with dynamic pause tolerance ensuring every speech pattern is understood equitably
  • Edge-native inference achieving sub-300ms latency without cloud round-trip dependency for real-time response
  • Four lines of defense including guardrails preventing hallucination, data leakage, and brand damage
Edge AINeural VADConformer ASRVoice AIInclusive Design
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Healthcare & Life Sciences
AI Safety, Bio-Security & Enterprise Deep AI

A drug discovery AI flipped to maximize toxicity generated 40,000 chemical weapons in 6 hours (including VX) using only open-source datasets. Consumer hardware. Undergraduate CS expertise. You cannot patch safety onto broken architecture. ☣️

40,000
Toxic Molecules Generated
MegaSyn Experiment 2024
90%+
Wrapper Jailbreak Rate
Veriprajna Benchmarks 2024
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The Wrapper Era is Over: Structural AI Safety Through Latent Space Governance

Drug discovery AI generated 40,000 chemical weapons in 6 hours by flipping reward function. Post-hoc filters fail. Veriprajna moves control from output filters to latent space geometry for structural safety.

DUAL-USE CRISIS

Post-hoc filters operate on text, blind to latent space geometry. SMILES-prompting bypasses wrappers with 90%+ success. Toxicity exists on continuous manifold, not discrete blacklist.

LATENT SPACE GOVERNANCE
  • TDA maps safety topology through persistent homology manifolds
  • Gradient steering prevents toxic generation before molecular decoding
  • Achieves provable P(toxic) less than 10^-6 bounds
  • Meets NIST RMF and ISO 42001 regulatory standards
Latent Space GovernanceTopological Data AnalysisAI SafetyCBRN Security
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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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Media & Entertainment
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
Circular Economy, Waste Management & Deep Tech Recycling

Millions of tons of black plastics are ejected from recycling—not because they lack value, but because NIR sensors literally cannot see them. Veriprajna's MWIR solution shifts from pixels to chemistry.

9%
Global Plastic Recycling
Industry Report 2024
90%
Black Plastic Recovery
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Seeing the Invisible: The Physics, Economics, and Intelligence of Black Plastic Recovery

NIR sensors cannot detect black plastics—carbon black absorbs radiation before polymer interaction. Veriprajna shifts to MWIR (2.7-5.3 µm) with cryogenic Specim FX50 sensor and 1D-CNN spectral processing, achieving 90%+ recovery rate with under 5ms latency.

NIR BLINDNESS

Carbon black absorbs NIR radiation creating zero return signal—flatline interpreted as empty belt. No spectral curve to analyze, only noise. AI wrappers cannot recover information lost at sensor layer.

MWIR CHEMICAL VISION
  • Shifts from NIR to MWIR (2.7-5.3µm) capturing polymer fundamental vibrations
  • Specim FX50 cryogenic sensor delivers 154 spectral bands at 380fps
  • 1D-CNN processes spectral signatures as signal not image achieving 90%+ recovery
  • Edge inference achieves under 5ms latency with TensorRT optimization on Jetson
MWIR Hyperspectral1D-CNN ProcessingCircular EconomySpecim FX50PLC IntegrationReal-Time InferenceGreen TechSustainable Recycling
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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.