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Advanced Platform for Data Development and Processing

Built by an engineer.
Designed for real-world problems.
Made for serious systems.

Founder: Milovan Tomašević, PhD

Concept: The unique advanexus platform precisely automates data integration, migration, and processing in complex environments — using algorithmic logic that traditional AI models cannot replicate.

Problem

  • Today’s data systems are: slow, complex, and unreliable.

  • The most common problems in real-world organizations:

    • Manual processes and Excel rely on people, not systems
    • Database migrations take months, with many errors
    • Integrating various sources is slow and complex
    • No standardization — every team works “their own way”
    • Business reports are delayed and unreliable
    • AI models lack quality input data
    • High costs of training and operational maintenance

Solution

  • advanexus: A unique platform that precisely automates data operations.

  • Key solution points:

    • Automatic data integration and migration without manual scripting
    • Algorithmic engine that handles dependencies, execution order, and data quality
    • A single environment for ingestion, processing, standardization, DQ, and visualization
    • Stable and deterministic processes reliable for banking, healthcare, and public sector
    • AI as an assistant, not the core – accelerates work but doesn’t control critical processes

What advanexus Enables

Use Case What the user gets Business Value Unique Value Proposition (UVP)
1. Data import and preparation (CSV/JSON → SQL table) Automatic file import and SQL generation Fast processing and merging without ETL tools Ingest + automatic SQL in a single step
2. Integration of diverse sources (DBs, APIs, files) One process linking DB2, Oracle, PostgreSQL, APIs, and files Elimination of silos and manual wiring Widest range of connectors + easy setup
3. Data standardization (Customer–Product–Transaction) Unified structure regardless of source 360° view without a lakehouse Internal standardized business model
4. Analytics and visualization SQL → charts, heatmaps, dashboards Fast insights, no external BI tool needed Visualization and data in one system
5. Backup & Restore Automated database copies and system recovery Security and stability Unique backup + restore engine
6. Automated SQL execution SQL scripts run sequentially or in parallel Faster operations, no manual work Centralized execution + error control
7. Cross-DB schema and data transfer Automatic transfer of tables and dependencies Major time savings in migrations Algorithmic dependency discovery
8. Dependency and phase management (Analyzer & Master) System auto-creates optimal execution order Maximum speed and reliability Intelligent parallelism scheduler
9. Data Quality rules Controls on every job and integration Accurate data and process security DQ directly integrated into pipeline
10. Scheduler – flow scheduling Jobs run automatically Full automation Simple, centralized scheduler
11. Python Sandbox (analytics & ML) Python environment within the platform Flexibility for data science team Unique mix of SQL + ingest + Python
12. Quick file analysis (Table view + SQL access) Every file becomes a table Instant analytics File-to-table SQL transformation
13. API integration (sending data outward) Sending SQL results to API endpoints Automatic integrations with other systems Formatting and sending without extra code
14. Knowledge Base (organizational memory) Documentation, tutorials, processes Faster onboarding, fewer mistakes First data platform with built-in knowledge

Industry Comparison

Criterion (Gartner DI/ETL) advanexus Databricks Snowflake Talend Informatica IICS Alteryx
1. File import (CSV/JSON/Excel/Logs) ✔ Native ingest 🔄 Spark ingest 🚫 Stage + COPY
2. Auto file → SQL table ✔ Unique 🚫 🚫 🚫 🚫 🚫
3. DB → DB data transfer ✔ Built-in 🚫 🚫 🔄 🔄 🚫
4. Schema and dependency migration ✔ Algorithmic 🚫 🚫 🚫 🚫 🚫
5. Orchestration (real-time jobs) ✔ Built-in 🔄 Workflows 🚫 SQL Tasks 🔄 🔄
6. Automated SQL script execution ✔ SQL Engine 🚫
7. Dependency and parallelism management ✔ Intelligent 🔄 Manual DAG 🚫 🔄 🔄 🚫
8. Built-in Data Quality rules ✔ Real-time DQ 🔄 (DLT) 🚫 🔄
9. Monitoring and alerting ✔ Centralized 🔄 🔄
10. Python sandbox / notebooks ✔ Built-in ✔ Notebooks 🚫 🚫 🚫 🚫
11. Built-in visualization (charts/dashboards) ✔ Visualizer 🔄 Basic 🚫 🚫 🚫

References:

  1. Databricks Documentation – Ingestion, Workflows, DLT
  2. Snowflake Documentation – Data Loading, Tasks
  3. Talend Documentation – Components & Data Quality
  4. Informatica IICS Documentation – Taskflows, Monitoring
  5. Alteryx Documentation – Designer, Server, Analytics

✔ = supported  🔄 = partially  🚫 = not supported

Data Platform Market

Market Size (base, $B) Size in 2030 ($B) CAGR (%) Fastest Growing Region Largest Market Source
Customer Data Platforms (CDP) 5.37 (2023) 51.95 39.5% Asia-Pacific North America Grand View Research
Autonomous Data Platforms 2.13 (2025) 5.37 20.3% Asia-Pacific North America Mordor Intelligence
Data Science Platforms 111.23 (2025) 275.67 21.4% Asia-Pacific North America Mordor Intelligence
DataOps Platforms 4.22 (2023) 17.17 22.5% Asia-Pacific North America Grand View Research
Data Integration Platforms (ETL/ELT) 15.18 (2024) 30.27 12.1% Asia-Pacific North America Grand View Research
Data Fabric Platforms 2.29 (2023) 12.91 (2032) 21.2% Asia-Pacific North America Fortune Business Insights
Master Data Management (MDM) 18.23 (2025) 43.38 18.9% Asia-Pacific North America Mordor Intelligence

Phase Goal: Transition to Production-Grade SaaS

  • Advanexus is moving from a prototype into a production-ready, scalable, and secure SaaS platform.
  • Focus: microservices, multi-tenant cloud architecture, AI layer, security (GDPR) and CI/CD.
  • We are building a fully isolated, modular environment on AWS aligned with Well-Architected best practices.
  • Goal: functional product + pilot users + Series A readiness within 12–18 months.

✅ Production version with AI layer (ETL/ELT, analytics, visualization)
✅ 3–5 pilot B2B clients
✅ Established cloud infrastructure (EKS, RDS, CI/CD, security)
✅ Series A preparation: validation, PR, traction

Team and Investment Plan for Transition to Production

Role / Position Key Responsibilities Duration Monthly Salary Total (18m)
CTO / Founder Development leadership, AI strategy 18 mo. €10,000 €180,000
Project Manager Roadmap and team coordination 18 mo. €6,000 €108,000
Lead Developer Architecture, microservices 18 mo. €6,000 €108,000
DevOps Engineer (AWS) CI/CD, monitoring, Terraform 18 mo. €4,000 €72,000
AI/ML Engineer AI/RAG/SQL prediction 18 mo. €3,500 €63,000
Full-Stack Developer Frontend + backend integration 18 mo. €3,000 €54,000
UI/UX Designer Interface design 18 mo. €3,000 €54,000
DB Administrator PostgreSQL, optimization, backup 18 mo. €3,000 €54,000
QA / Support Testing, regressions, documentation 18 mo. €1,800 €32,400
Security Advisor GDPR, encryption, auditing 18 mo. €2,500 €45,000
BizDev / Sales Contracts, partnerships, validation 18 mo. €3,500 €63,000
Marketing / PR Promotion, campaigns, events 18 mo. €2,000 €36,000
AWS Cloud Support Cloud optimization consulting 18 mo. €1,200 €21,600
HR / Admin / Legal Team, admin, legal oversight 18 mo. €2,000 €36,000
Total (net) €927,000
+ Contingency (~15%) Licenses, equipment, workspace costs ~€140,000
Total Investment Production + pilot clients + Series A validation ~€1,070,000

Expected Outcomes (12–18 Months)

  • Production-grade platform with support for multi-tenant models and complex data flows
  • 🧠 Integrated AI engine for SQL assistance, analytics, and automated migrations
  • 🌐 Complete AWS infrastructure, deploy-ready for Azure/GCP (EKS, RDS, S3, CI/CD)
  • 🔐 Enterprise-grade security, including RBAC, audit logs, encryption, and GDPR compliance
  • 📈 3–5 active pilot B2B clients across sectors (banking, healthcare, analytics)
  • 🤝 Signed initial contracts and validated MRR (~€30k monthly)
  • 🔍 Market visibility through 3+ conferences and initial media exposure
  • 💼 Established organization: development, sales, support, DevOps, security, QA
  • 🚀 Pitch and PR ready for Series A round — market validation and commercial traction

Conclusion – Why Invest in Advanexus?

advanexus is not just another data platform — it is a fundamentally new approach to data integration and processing in complex environments. In a world where organizations spend weeks on manual workflows, advanexus enables automated, accurate, and fast data operations through:

  • 🧠 AI that enhances work, rather than replacing people
  • ⚙️ ETL, analytics, and visualization in a single product
  • 🚀 Production-grade SaaS ready for market and customers
  • 📈 A multi-billion dollar market with accelerating growth
  • 🤝 A proven expert team with real-world results
  • 📦 Initial contracts, pilot clients, and a clear monetization model

With this investment, you are enabling the scaling of a solution the industry truly needs – not for the hype, but for real value.

🎯 Investment sought: ~€1,070,000 🎯 Goal: Production launch, pilot clients, and Series A readiness

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