From predictive demand forecasting and multi-spectral vision to autonomous procurement agents — we turn proven AI research into production systems that generate measurable returns in 30–90 days.
Strategic Engineering // Florida Registry L26000104293
Five battle-tested solutions built on our proprietary stack. Each can be piloted in 30–60 days and scaled enterprise-wide.
Not sure which solution fits your business best?
Demand forecasting and AI-powered decision engine for online marketplaces and e-commerce sellers.
Reduced overstock by 34% and stockouts by 41% on high-volume platforms.
Autonomous supplier discovery, price trend prediction and seamless ERP integration for warehouses and modern supply chains.
Early fault and leak detection using acoustic emission analysis and P-F curve modeling for critical industrial assets.
Advanced UV + IR multi-spectral inspection with real-time edge AI for quality control across manufacturing and industrial applications.
No endless consulting. No failed pilots.
We deliver production AI fast — with measurable business impact at every step.
Deep-dive discovery session + technical audit. You receive a personalized AI Maturity Score, prioritized use cases, and clear ROI projections.
We deploy one high-impact solution in a controlled environment with your real data to prove measurable ROI.
Seamless integration with your ERP, marketplaces, or industrial systems. Team training and production-ready pipelines.
Ongoing monitoring, model retraining, and expansion to new use cases. We stay with you as a long-term AI partner.
RESEARCH PAPER
10-seed benchmark on held-out physical validation (N=61): at 20% procedural synthetic contamination, Recall falls from 97.95% to 85.13% while missed leaks surge 7.25-fold; Grad-CAM and spectral sweep confirm 2–3 kHz shortcut learning.
RESEARCH PAPER
Just 1% heuristic synthetic WAV files inflate internal accuracy to 99.67% — while out-of-domain Recall collapses from 70% to 11.43% at 20% contamination (WDN Hong Kong).
RESEARCH PAPER
Physics-informed post-decision arbitration cut false positives by 20.9% on 110 field records while preserving 63.3% recall — when only 2.9% of historical files have verified distance labels.
FIELD GUIDE
Structured pre-winter risk assessment framework for municipal district heating networks — prioritizing sections for inspection before the heating season.
CASE STUDY
When ensemble AI models and a third-party correlator all flag a leak on a flooded 336 m district heating line, hybrid PNR (8.64) and phase coherence (0.4%) produce the correct no-excavation verdict.
RESEARCH PAPER
Field-validated RIVIXI Diagnose module maps corrosive wall thinning (10–18.5% loss) on a ø426×7 mm district heating line — converging with UT measurements within 3.5% while ruling out active leakage.
RESEARCH PAPER
How Zero-Crossing Rate (ZCR) verification in RIVIXI AI v1.3 distinguishes hydrodynamic leak hiss from impulsive mechanical false alarms — reducing FPR by 13.25% on 113 field recordings.
CASE STUDY
How Decision Fusion algorithms process messy utility maintenance logs and GIS records to achieve 93.88% Recall on pre-failure zone detection.
RESEARCH PAPER
How modern neural network algorithms eliminate the pipeline false alarm scalability barrier inherent in the direct reengineering of classical non-destructive testing.
RESEARCH PAPER
How Rivixi Lab overcame Nyquist limit collisions and legacy sensor constraints to integrate SebaKMT and Kaskad-3 detectors into the RIVIXI AI v1.3 SaaS platform.
RESEARCH PAPER
How a multi-model blending ensemble combined with rigorous data sanitization improved district heating and water pipeline risk prediction, achieving a ROC-AUC of 0.8879.
RESEARCH PAPER
How converting ultrasonic signals into Mel-Spectrograms and applying ensemble neural networks overcame the limitations of traditional heuristics in noisy industrial environments.
RESEARCH PAPER
MBN-RVX-AI replaces slow replica metallography during outages with cloud AI screening—over 92% accuracy on 2.25Cr-1Mo steel microstructural degradation.
CASE STUDY
How moving from scripted chatbots to autonomous ReAct agents with Advanced RAG enables zero-hallucination tech support for complex enterprise software.
RESEARCH PAPER
A hybrid machine learning architecture combining static pipeline characteristics with temporal accident history to forecast failures in urban heat and water supply systems.
Our team has spent over 15 years turning cutting-edge AI research into production systems that generate real ROI.
Founder, RIVIXI LLC | AI Systems Architect (Industrial AI)
PhD in Materials Science • 8 patents • 90+ publications
Bridging over 20 years of hands-on experience in materials science, industrial systems, and non-destructive testing with modern AI to build production-ready SaaS solutions for manufacturing, quality inspection, and industrial data analytics.
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Co-Founder & AI Architecture Lead | AI Systems Architect
Senior AI Engineer · Production systems & live deployment
Senior AI Engineer and architect of production-grade AI pipelines. Sergei bridges the gap between R&D and live deployment. He specializes in building scalable, high-performance systems: multi-agent orchestration, low-latency edge inference, and hybrid LLM + Computer Vision deployments.
Expertise in real-time WebRTC pipelines and end-to-end automation for business: from demand forecasting to autonomous procurement and multispectral quality control.
Ready to work with the team that actually builds and ships production AI?