Enterprise Platform for Data Control and Automation

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Enables full control, automation, and reliability of data processes
in complex and highly regulated environments.

Built for enterprise environments. Designed for full control.

Founder: Milovan Tomašević, Phd

The Real Enterprise Problem

Problem (Current State) Impact (Why It Matters)
Data scattered across dozens of systems High operational costs
Manual or fragile processes Long time-to-value
Difficult to audit and poorly documented Increased regulatory risk
Slow to change, migrate, and analyze Dependency on individuals

Result: systems are slow, complex, and unreliable.
Root cause: not a lack of tools, but a lack of control and orchestration above the existing stack.

Solution

Today’s Problem How advanexus Solves It
Fragmented systems and data sources A single controlled layer across all existing systems
Manual, fragile, and unreliable processes Automated and deterministic process execution
Unclear execution order and dependencies Algorithmic engine for dependency resolution
Weak data quality control Built-in data quality rules in every process
Lack of traceability and auditability Full audit trail for every step
Slow analytics and ad-hoc tools Analytics, sandbox, and visualization in one environment

advanexus is a control & automation layer above existing data systems.
No replatforming. No replacement of core systems.

How Advanexus Delivers Value

How It Works (End-to-End) What It Delivers (Core Value)
Ingest & Integration (DB / API / files) Reduced operational complexity
Orchestration (dependencies, scheduler) Faster migrations and changes
Data Quality (rules, controls, notifications) Lower regulatory and operational risk
Execution Engine (SQL + cross-DB) Faster access to trusted data
Evidence & Audit (proof of every step) Reduced dependency on individuals
Sandbox & Visualization (analytics in one system) Faster results and better decisions

What It Enables (Grouped Use Cases):

  • Integration & ingest: files → SQL tables, DB/API connectors, fast file analysis
  • Migration & automation: cross-DB schema/data transfer, deterministic SQL execution, scheduling
  • Reliability & compliance: data quality in pipelines, audit evidence, backup & restore
  • Analytics & knowledge: visualization and dashboards, Python sandbox, knowledge base

Simpler data management. Higher reliability. Faster outcomes.

Who Are Advanexus Customers

Primary customers are organizations with complex and regulated data environments:

  • Banks (Tier-1 and Tier-2)
  • Insurance companies
  • Fintech and payment providers
    • High regulatory requirements
    • Heterogeneous systems
  • Large enterprise organizations

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

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

Traction – Where We Are Today

  • Functional MVP and demo environment
  • Proven real-world use cases:
    • data and schema migrations
    • automated SQL execution
    • data quality controls and audit evidence
  • Repeatable PoC models (no custom development)
  • Tested in real enterprise scenarios

Status: Advanexus is an operational platform in an early validation phase, ready for PoC and pilot implementations with enterprise customers.

Business Model

Element How the Model Is Structured
Customer entry point PoC and pilot phases as a controlled entry
Core revenue Enterprise licensing (annual subscription)
Expansion per customer “Land & expand” by team, use case, and scale
Contract structure Long-term agreements (3–5 years)
Deployment Partner-led deployment or supported by the core team
Additional revenue Implementation, onboarding, premium support
Operational characteristics High gross margins, low churn, high LTV

Go-To-Market Model

Phase Approach Objective
Early entry Founder-led enterprise selling Initial PoC and pilot validation
Entry point Focus on key (wedge) use cases Fast value with low risk
Standardization PoC factory Repeatable and controlled entry
Scaling Partners (SIs and regional integrators) Expansion without internal team growth
Growth Reference-driven sales Credibility and faster deal closure

Principle: small initial footprint → proven value → controlled expansion.

Team

  • Founder with deep enterprise and banking experience
  • Small, senior team of experts (platform, DevOps, data & AI)
  • Strong focus on reliability, scalability, and regulated environments

Capital Requirements

  • Scaling the existing MVP into an enterprise-ready platform
  • PoC and pilot validation in the market
  • Building the sales and partner model

Exit Strategy

Element Description
Primary exit Strategic acquisition by enterprise, data, or cloud vendors
Buyer profile Companies seeking a control and automation layer above existing systems
Alternative exits Growth or buy-and-build Private Equity
Secondary exit in a later growth stage
Acquisition rationale Modular and integrable architecture
Focus on regulated industries
Filling a critical gap in existing data platforms
Repeatable enterprise use cases and references
Time horizon Mid-term horizon (5–7 years)

Development and Validation Roadmap

Activity Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8
MVP and core architecture stabilization ✔️ ✔️
PoC factory (standardized use cases) ✔️ ✔️ ✔️
Customer pilot implementations ✔️ ✔️ ✔️
Multi-tenant hardening and isolation ✔️ ✔️ ✔️
Security, audit, and compliance readiness ✔️ ✔️ ✔️
Partner model and enablement ✔️ ✔️ ✔️
Sales scaling (GTM execution) ✔️ ✔️ ✔️ ✔️
Internationalization and regional rollout ✔️ ✔️ ✔️
Exit readiness (IP, structure, references) ✔️ ✔️ ✔️

Call to Action

advanexus is building a control and automation layer for the next generation of enterprise data operations.

We are looking for:

  • strategic investors,
  • design-partner customers,
  • long-term partners for joint growth.

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