From data To decision

From data To decision

Build AI governance in your organization so that AI can lead core operations. The world is changing. Don't fall behind this time.

Start discussing AI strategy

“We are in the beginning of the first act of a revolution.”

Alex Karp

-

CEO, Palantir

“The age of AI has started.”

Jensen Huang

-

CEO, NVIDIA

“We’re in a new phase of the AI platform shift.”

Sundar Pichai

-

CEO, Google & Alphabet

“We are in the beginning of the first act of a revolution.”

Alex Karp

-

CEO, Palantir

“The age of AI has started.”

Jensen Huang

-

CEO, NVIDIA

“We’re in a new phase of the AI platform shift.”

Sundar Pichai

-

CEO, Google & Alphabet

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Is This You?

Are you having trouble building a dedicated AI team?

Are you having trouble building a dedicated AI team?

Is data quality a concern for your organization?

Is data quality a concern for your organization?

Are you stuck in data silos?

Are you stuck in data silos?

Do you want to apply AI to your legacy systems?

Do you want to apply AI to your legacy systems?

Do you want to apply AI to your legacy systems?

Are you worried about data leakage when applying AI technologies?

Are you worried about data leakage when applying AI technologies?

[ PAIN POINTS ]

Difficulty in building an AI expert team

Challenge – Building and operating AI systems requires experienced data scientists, ML engineers, and system architects, but such talent is in short supply.

Solution – CartaNova dispatches expert teams to directly execute projects, resolving internal talent shortages.

Challenge – Poor-quality or unstructured training data can lead to inaccurate AI outputs or hallucinations.

Solution – CartaNova refines and structures large-scale data stored in the Data Lake using ontologies to enable high-quality learning environments.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

Challenge – Transitioning from legacy systems to AI-based systems typically requires extensive time and resources.

Solution – CartaNova integrates AI without altering existing architectures, enabling smooth and efficient transitions.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

[ PAIN POINTS ]

Difficulty in building an AI expert team

Challenge – Building and operating AI systems requires experienced data scientists, ML engineers, and system architects, but such talent is in short supply.

Solution – CartaNova dispatches expert teams to directly execute projects, resolving internal talent shortages.

Challenge – Poor-quality or unstructured training data can lead to inaccurate AI outputs or hallucinations.

Solution – CartaNova refines and structures large-scale data stored in the Data Lake using ontologies to enable high-quality learning environments.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

Challenge – Transitioning from legacy systems to AI-based systems typically requires extensive time and resources.

Solution – CartaNova integrates AI without altering existing architectures, enabling smooth and efficient transitions.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

[ PAIN POINTS ]

Difficulty in building an AI expert team

Challenge – Building and operating AI systems requires experienced data scientists, ML engineers, and system architects, but such talent is in short supply.

Solution – CartaNova dispatches expert teams to directly execute projects, resolving internal talent shortages.

Challenge – Poor-quality or unstructured training data can lead to inaccurate AI outputs or hallucinations.

Solution – CartaNova refines and structures large-scale data stored in the Data Lake using ontologies to enable high-quality learning environments.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

Challenge – Transitioning from legacy systems to AI-based systems typically requires extensive time and resources.

Solution – CartaNova integrates AI without altering existing architectures, enabling smooth and efficient transitions.

Challenge – Entrusting external parties with data can introduce serious security risks.

Solution – CartaNova never collects or stores institutional data externally; instead, it connects directly to internal systems to ensure maximum security.

[ WORK PARTS ]

Supports You

Data Lake Construction

We integrate and refine all organizational data.

Data Lake Construction

We integrate and refine all organizational data.

Data Lake Construction

We integrate and refine all organizational data.

Data Ontology

We transform all organizational data into an ontology that AI can understand.

Data Ontology

We transform all organizational data into an ontology that AI can understand.

Data Ontology

We transform all organizational data into an ontology that AI can understand.

Custom AI Agent Development

We build custom AI agent based on all organizational data.

Custom AI Agent Development

We build custom AI agent based on all organizational data.

Custom AI Agent Development

We build custom AI agent based on all organizational data.

Legacy systems Integration

We integrate the organization's legacy systems and control them through a customized AI agent.

Legacy systems Integration

We integrate the organization's legacy systems and control them through a customized AI agent.

Legacy systems Integration

We integrate the organization's legacy systems and control them through a customized AI agent.

Code

1

2

3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

New System Development

If necessary, we develop new systems. CartaNova has extensive experience in delivering SaaS projects (e-commerce, healthcare, OTT, social networking, mobility) as well as enterprise software solutions.

Code

1

2

3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

New System Development

If necessary, we develop new systems. CartaNova has extensive experience in delivering SaaS projects (e-commerce, healthcare, OTT, social networking, mobility) as well as enterprise software solutions.

Code

1

2

3

4

5

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

New System Development

If necessary, we develop new systems. CartaNova has extensive experience in delivering SaaS projects (e-commerce, healthcare, OTT, social networking, mobility) as well as enterprise software solutions.

Our Tech
Stack

[05]

Node.js

[04]

MySQL

[03]

Databricks

[02]

Snowflake

[01]

LangChain

[05]

Node.js

[04]

MySQL

[03]

Databricks

[02]

Snowflake

[01]

LangChain

[05]

Node.js

[04]

MySQL

[03]

Databricks

[02]

Snowflake

[01]

LangChain

[ CORE TECH ]

Ontology Builder

A tool for creating, storing, and managing ontology data, supporting the structured representation and utilization of domain knowledge.

Ontology Builder

A tool for creating, storing, and managing ontology data, supporting the structured representation and utilization of domain knowledge.

Ontology-Data Connector

A tool that links various raw data to the ontology, enabling semantic data integration across systems.

Ontology-Data Connector

A tool that links various raw data to the ontology, enabling semantic data integration across systems.

RAG / MCP / A2A

A system that generates and stores Graph-RAGs based on the constructed ontology. Supports both vector search and graph-based expanded retrieval. And supports MCP and A2A protocol.

RAG / MCP / A2A

A system that generates and stores Graph-RAGs based on the constructed ontology. Supports both vector search and graph-based expanded retrieval. And supports MCP and A2A protocol.

Unified Agent

An intelligent agent framework that collects contextual information based on ontology RAG and executes workflows by extracting data from the semantic layer through predefined pipelines. Compatible with all model types including LLMs, sLLMs, and LAMs.

Unified Agent

An intelligent agent framework that collects contextual information based on ontology RAG and executes workflows by extracting data from the semantic layer through predefined pipelines. Compatible with all model types including LLMs, sLLMs, and LAMs.

Layouts

Styles

Variables

Workflow Integration

Seamlessly integrates with legacy systems (ERP, CRM, SCM, HR, FI, etc.), delivering organization-specific features through automated flows.

Layouts

Styles

Variables

Workflow Integration

Seamlessly integrates with legacy systems (ERP, CRM, SCM, HR, FI, etc.), delivering organization-specific features through automated flows.

Data Lake Support

Provides direct support for building data governance frameworks, especially for organizations that are in the early stages of or struggling with data lake implementation.

Data Lake Support

Provides direct support for building data governance frameworks, especially for organizations that are in the early stages of or struggling with data lake implementation.

Sonamu Framework

CartaNova’s own global web framework, built to overcome limitations of traditional Node.js frameworks while incorporating their strengths.

Sonamu Framework

CartaNova’s own global web framework, built to overcome limitations of traditional Node.js frameworks while incorporating their strengths.

Sonamu Framework

CartaNova’s own global web framework, built to overcome limitations of traditional Node.js frameworks while incorporating their strengths.

See Our Work in Action

[ BLOG ]

Latest Insights

More Insights

[

ARTICLE

]

Building Data Governance Architecture on AWS

This diagram illustrates an end-to-end architecture designed to establish robust data governance using a suite of Amazon Web Services (AWS) tools. The structure enables organizations to collect, ingest, store, process, analyze, and visualize data in a secure and scalable environment. The entire flow is divided into six major stages, each fulfilling a key function in the data lifecycle.

[

ARTICLE

]

Building Data Governance Architecture on AWS

This diagram illustrates an end-to-end architecture designed to establish robust data governance using a suite of Amazon Web Services (AWS) tools. The structure enables organizations to collect, ingest, store, process, analyze, and visualize data in a secure and scalable environment. The entire flow is divided into six major stages, each fulfilling a key function in the data lifecycle.

[

ARTICLE

]

Building Data Governance Architecture on AWS

This diagram illustrates an end-to-end architecture designed to establish robust data governance using a suite of Amazon Web Services (AWS) tools. The structure enables organizations to collect, ingest, store, process, analyze, and visualize data in a secure and scalable environment. The entire flow is divided into six major stages, each fulfilling a key function in the data lifecycle.

[

PAPER

]

Ontology Development 101: A Guide to Creating Your First Ontology

A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.

[

PAPER

]

Ontology Development 101: A Guide to Creating Your First Ontology

A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.

[

PAPER

]

Ontology Development 101: A Guide to Creating Your First Ontology

A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.

[

PAPER

]

Self‑Rewarding Language Models

This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.

[

PAPER

]

Self‑Rewarding Language Models

This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.

[

PAPER

]

Self‑Rewarding Language Models

This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.

Comparison

See how we compare against others

See how we compare against others

See how we compare against others

Bottom-up, execution-driven

Bottom-up, execution-driven

Bottom-up, execution-driven

End-to-end service covering everything from consulting to actual product development

End-to-end service covering everything from consulting to actual product development

End-to-end service covering everything from consulting to actual product development

Agile, custom solution development

Agile, custom solution development

Agile, custom solution development

Selective but deep, long-term partnerships

Selective but deep, long-term partnerships

Selective but deep, long-term partnerships

Low cost

Low cost

Low cost

Others

Top-down, consulting-driven

Top-down, consulting-driven

Top-down, consulting-driven

Providing only limited services focused on either consulting or development

Providing only limited services focused on either consulting or development

Providing only limited services focused on either consulting or development

Large-scale, general-purpose solution development

Large-scale, general-purpose solution development

Large-scale, general-purpose solution development

Broad but shallow, consulting-driven relationships

Broad but shallow, consulting-driven relationships

Broad but shallow, consulting-driven relationships

High cost

High cost

High cost

[ LAYERS ]

We Are Here — This Layer

We Are Here
— This Layer

Infrastructure Layer

Foundation (Model) Layer

Foundation (System) Layer

Application Layer

Infrastructure Layer

Foundation (Model) Layer

Foundation (System) Layer

Application Layer

Infrastructure Layer

Foundation (Model) Layer

Foundation (System) Layer

Application Layer

Application Layer

AI-powered services and user-facing solutions built on top of models

Application Layer

AI-powered services and user-facing solutions built on top of models

Foundation (System) Layer

Data integration, processing, and orchestration systems that prepare data and environments for AI application development

Foundation (System) Layer

Data integration, processing, and orchestration systems that prepare data and environments for AI application development

Foundation (Model) Layer

Large-scale models, model training & deployment platforms

Foundation (Model) Layer

Large-scale models, model training & deployment platforms

Infrastructure Layer

Hardware, cloud services, computing resources

Infrastructure Layer

Hardware, cloud services, computing resources

[ CONTACT US ]

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