RIVIXI
SYSTEMS
RIVIXI
SYSTEMS
RIVIXI
SYSTEMS
Rivixi Systems Logo

Rivixi Systems:
Shaping the AI-First Future

Engineering venture studio developing AI software, diagnostic systems, and integrated industrial solutions.

Technology

Core AI Verticals

Intelligence in Data, Vision, and Signal

01 / ACOUSTIC

Acoustic Signal Intelligence

Predictive failure modeling & signal interpretation

Acoustic signal analysis to support leak investigation and localization in pipeline networks.

  • Pattern Recognition: Neural classification of acoustic emission and vibration signatures with temporal sequence modeling.
  • P–F Curve Modeling: Failure prediction aligned with P–F curve methodology for maintenance planning and risk scoring.
  • Leak Investigation: Signal analysis to support leak investigation and localization across distributed industrial sensor networks.

Stack: AE signal models · Temporal encoding · Predictive risk scoring · Proprietary IP

02 / DATA

Data Intelligence

Time-series modeling & decision systems

Neural forecasting pipelines designed for high-variance data environments, enabling structured decision support across financial, retail, and operational datasets.

  • Acquisition: API-based ingestion pipelines with compliant data collection and normalization across financial and operational sources.
  • Time-Series Forecasting: Short- and mid-term forecasting across daily, weekly, and monthly horizons using LSTM and encoder–decoder architectures with production-ready inference pipelines.
  • Pattern Analysis: Behavioral and market signal extraction with ranking and scenario-based decision support systems.

Stack: Hybrid LSTM Encoder–Decoder · Time-series pipelines · Proprietary IP

03 / VISION

Computer Vision & Imaging

Multi-spectral analysis & spatial inference

End-to-end computer vision pipelines from calibrated capture to neural segmentation, designed for diagnostic and industrial inspection environments.

  • Multi-spectral Capture: Imaging pipelines across UV, visible, infrared, and extended spectral bands for subsurface, thermal, and texture-level signal extraction.
  • Real-time Edge: Low-latency streaming with WebRTC and edge-first inference designed for interactive and real-time processing constraints.
  • Neural Segmentation: Encoder–decoder segmentation models with ROI detection and boundary-level precision for defects and diagnostics.

Stack: Multi-spectral CV pipelines · Edge inference · Spatial processing · Proprietary IP

Rivixi.com

Ventures Portfolio

Full-stack ventures built on shared intelligence infrastructure — from live marketplace engines to clinical-grade vision systems.

  • Venture

    RIVIXI Pipeline Intelligence

    AI-assisted pipeline diagnostics and inspection planning.

    • Predict

      Risk-based inspection priorities from pipeline data.

    • Locate

      Acoustic signal analysis for leak investigation.

    • Diagnose

      Condition screening to guide follow-up inspection.

    Explore Platform
  • Venture

    SellersAI

    Marketplace analysis to support product selection and launch planning.

    • Market scanning

      Current prices, sellers, and product listings on Kaspi.kz.

    • Demand intelligence

      Trend and demand assessment from live marketplace data.

    • Decision support

      Go/no-go assessments and product launch plans.

    Explore Platform
  • Venture

    NeuroDerm

    AI-assisted skin image analysis and observation tracking.

    • UV imaging

      Skin fluorescence imaging at 365 nm.

    • Image analysis

      AI-assisted assessment of skin images.

    • Observation tracking

      Images and symptom history for follow-up with a clinician.

    Explore Platform
  • Venture

    Zakup AI

    Demand and inventory analysis to support procurement planning.

    • Demand analysis

      Forecasting from sales history and inventory data.

    • Inventory monitoring

      Identification of shortages, overstock, and anomalies.

    • Procurement planning

      Replenishment recommendations using data from business systems.

    Explore Platform

Labs / R&D

Research & Engineering

LLM infrastructure

Fine-tuning, RAG & orchestration

Domain adaptation and grounded generation on proprietary corpora, with throughput-bound serving and explicit evaluation gates per release.

  • Instruction tuning and supervised fine-tuning on curated, versioned datasets.
  • RAG: hybrid dense/sparse retrieval, chunking policy, re-ranking, and grounded response contracts.
  • Multi-agent pipelines: planner–worker graphs, tool routing, structured outputs, and policy guardrails.
  • Neural stack optimization: batching, KV-cache discipline, quantization-aware paths where accuracy budgets allow.

Fixed eval harnesses (n≥500 queries per tier); retrieval and grounding metrics tracked per deploy.

Computer vision

Prototypes & spatial inference

R&D builds from calibrated capture through segmentation and geometry—validated on internal holdouts before production handoff.

  • Multi-spectral capture (e.g. UV–NIR) with radiometric calibration for inspection and QC use cases.
  • Neural segmentation: encoder–decoder stacks, attention refinement, ROI and boundary metrics on fixed splits.
  • Spatial computing: registration, multi-view fusion, depth-aware geometry where sensors permit.
  • Low-latency paths: WebRTC preview and edge offload when sub-100 ms interaction is a hard constraint.

Internal holdout mAP tracked release-to-release; parity checks on sensor profile changes.

Autonomous agents

Multi-agent systems & engines

Automation with explicit state, auditable decisions, and deterministic fallbacks over stochastic model output.

  • Task graphs and state machines across agents; rollback and idempotency on external side effects.
  • Decision engines: scoring, ranking, and Go/No-Go gates aligned to operational and risk constraints.
  • Tool automation: REST/GraphQL integrations, document workflows, procurement- and ERP-adjacent hooks.
  • Run logs, prompt/tool traces, and replay against policy versions for review.

Traces exportable; policy version pinned per production run.

Scalability

Deployment & inference SLOs

Hybrid AWS/Vercel footprint with autoscaling, observability, and documented latency/cost trade-offs by workload tier.

  • Topology: regional compute on AWS, edge delivery and serverless burst via Vercel where appropriate.
  • Low-latency inference: connection pooling, warm pools, CDN and cache layers for hot paths.
  • Tiered serving: interactive vs. batch queues; explicit p95/p99 budgets per surface.
  • Monitoring: distributed tracing, drift and regression suites on frozen evaluation sets.

p95 latency and $/1M tokens (or equivalent) recorded per tier; regressions block promote.

Trust & infrastructure

Security & Compliance

  • Enterprise-Grade Security

    All data is protected using AES-256 encryption and processed within secure cloud infrastructure hosted in the United States (AWS).

    End-to-end data protection.

  • Regulatory Compliance

    Our systems are designed to meet strict regulatory requirements, including HIPAA readiness for healthcare applications and financial data standards.

    Built for regulated environments.

  • US-Based Operations

    Rivixi LLC is a registered entity in the United States (Florida), ensuring full legal transparency and operational accountability.

    Transparent by structure.

All systems are deployed within US-based infrastructure and follow strict internal security protocols.

Leadership & Core Engineering

Aleksandr Ivanaiskii

Aleksandr Ivanaiskii, PhD

Founder & CEO, RIVIXI LLC | AI Systems Architect

  • Credentials: PhD in Materials Science • 8 patents • 90+ scientific publications

  • Focus: I bridge deep industrial domain expertise with modern AI to create SaaS solutions that make quality inspection, defect detection, and predictive analytics smarter and more profitable for manufacturers.

Sergey Shipilov

Sergey Shipilov

Co-Founder / COO / AI Architect

  • AI architecture: Predictive systems engineered into deployable business logic.

  • End-to-end: Multi-agent pipelines packaged as focused enterprise surfaces.

From Engineering Challenge to Working System

Bring us your engineering challenge, data, and operating requirements.Together, we can define a pilot, validate the approach, and plan integration.