Your AX Partner

Your AX Partner

From strategy to deployment, we turn real business operations into AI-ready systems.

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Is This You?

Struggling to build a dedicated AI team?

Struggling to build a dedicated AI team?

Concerned about the quality and readiness of your data?

Concerned about the quality and readiness of your data?

Stuck with siloed data across departments and systems?

Stuck with siloed data across departments and systems?

Having difficulty applying AI to legacy systems and workflows?

Having difficulty applying AI to legacy systems and workflows?

[ WORK PARTS ]

Supports You

AI Infrastructure Development

We build the AI infrastructure your organization needs to scale.

AI Infrastructure Development

We build the AI infrastructure your organization needs to scale.

Data Ontology & RAG Pipeline Development

We refine all your data to make it AI-ready.

Data Ontology & RAG Pipeline Development

We refine all your data to make it AI-ready.

Data Ontology & RAG Pipeline Development

We refine all your data to make it AI-ready.

Custom AI Agent Development

We design and build custom AI agents optimized for each client’s data, workflows, and operational environment using the latest AI models, fine-tuning, RAG, and tool integration.

Legacy System AI Transformation & Integration

We redesign existing systems into AI-ready architectures and unify fragmented data, workflows, and functions to build an operational foundation for AI transformation.

Code

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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}"

Industry-Agnostic System Development

We design and build new systems across industries, tailored to each client’s workflow, data, and operational requirements.

Code

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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}"

Industry-Agnostic System Development

We design and build new systems across industries, tailored to each client’s workflow, data, and operational requirements.

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}"

Industry-Agnostic System Development

We design and build new systems across industries, tailored to each client’s workflow, data, and operational requirements.

Our Tech
Stack

[05]

Voyage AI

[04]

PostgreSQL

[03]

Sonamu Framework

[02]

TypeScript

[01]

Node.js

[05]

Voyage AI

[04]

PostgreSQL

[03]

Sonamu Framework

[02]

TypeScript

[01]

Node.js

[05]

Voyage AI

[04]

PostgreSQL

[03]

Sonamu Framework

[02]

TypeScript

[01]

Node.js

[ CORE TECH ]

Sonamu Framework

A proprietary full-stack framework that delivers end-to-end type safety from database to frontend. With database-first development, auto-generated APIs, a built-in admin panel, integrated validation, and observation-based testing via Naite, it removes traditional Node.js setup overhead while dramatically reducing testing costs. Built-in AI readiness enables every Sonamu service to become instantly accessible to SocratsAI through automatic schema introspection and semantic layer generation.

Q&A-Based RAG

Question-to-question vector matching instead of document chunking. Store curated Q&A pairs and retrieve through question similarity—aligning directly with how LLMs learn.

AI-Native Ontology

Structured knowledge using question-answer pairs, not complex graphs. Simple format that aligns with LLM training mechanisms—our answer to Palantir-style complexity. Enables reliable retrieval through pgvector semantic search.

AI-Powered Reconstruction

AI-assisted system reconstruction for legacy databases. Automatic schema introspection, AI-powered documentation generation, and full-stack scaffolding enable complete modernization without business disruption.

Multi-Model Integration

Seamless orchestration of ChatGPT, Claude, and Gemini through unified interface. Model switching, cost tracking, and performance comparison—no vendor lock-in, optimal task-model matching.

Layouts

Styles

Variables

Legacy System Integration

Direct connections to enterprise systems (ERP, CRM, SCM, HR, FI) with semantic layer construction. Our Q&A-based ontology enables natural language queries to complex databases—no SQL knowledge required.

AI-Assisted Development

AI is embedded in every layer of our development workflow—from pair programming with Cursor and Claude to automated testing with Naite and AI-powered code review. This enables us to deliver not just AI features, but truly AI-native systems for our clients.

[ LAYERS ]

We Are Here

We Are Here
— This Layer

Infrastructure Layer

Model Foundation Layer

System Foundation Layer

Application Layer

Infrastructure Layer

Model Foundation Layer

System Foundation Layer

Application Layer

Infrastructure Layer

Model Foundation Layer

System Foundation Layer

Application Layer

Application Layer

AI-powered applications, developer tools, and user-facing services built on top of foundation models (Cursor / GitHub Copilot / Perplexity / Harvey)

Application Layer

AI-powered applications, developer tools, and user-facing services built on top of foundation models (Cursor / GitHub Copilot / Perplexity / Harvey)

System Foundation Layer

Enterprise AI system platforms for data integration, orchestration, governance, and operationalization (Palantir Foundry / Snowflake / Databricks / Dataiku)

System Foundation Layer

Enterprise AI system platforms for data integration, orchestration, governance, and operationalization (Palantir Foundry / Snowflake / Databricks / Dataiku)

Model Foundation Layer

Large-scale foundation model families that power modern AI systems (OpenAI GPT / Google Gemini / Anthropic Claude / xAI Grok)

Model Foundation Layer

Large-scale foundation model families that power modern AI systems (OpenAI GPT / Google Gemini / Anthropic Claude / xAI Grok)

Infrastructure Layer

Cloud, GPU, and compute infrastructure for training and serving large-scale AI systems (NVIDIA / AWS / Microsoft Azure / Google Cloud)

Infrastructure Layer

Cloud, GPU, and compute infrastructure for training and serving large-scale AI systems (NVIDIA / AWS / Microsoft Azure / Google Cloud)

See Our Work in Action

[ PROJECTS ]

Our Projects

Oncology Patient Matching Project for New Drug Clinical Trials

An AI-powered oncology clinical infrastructure project designed to connect cancer patients, clinical trials, and specialist collaboration in Japan. The project aims to structure hospital data, trial protocols, DICOM and clinical documents, and create a secure, workflow-oriented platform for AI-assisted trial screening, Doctor-to-Doctor (DtoD) consultation, OCR-based data extraction, and hospital-centered interoperability.

Oncology Patient Matching Project for New Drug Clinical Trials

An AI-powered oncology clinical infrastructure project designed to connect cancer patients, clinical trials, and specialist collaboration in Japan. The project aims to structure hospital data, trial protocols, DICOM and clinical documents, and create a secure, workflow-oriented platform for AI-assisted trial screening, Doctor-to-Doctor (DtoD) consultation, OCR-based data extraction, and hospital-centered interoperability.

Oncology Patient Matching Project for New Drug Clinical Trials

An AI-powered oncology clinical infrastructure project designed to connect cancer patients, clinical trials, and specialist collaboration in Japan. The project aims to structure hospital data, trial protocols, DICOM and clinical documents, and create a secure, workflow-oriented platform for AI-assisted trial screening, Doctor-to-Doctor (DtoD) consultation, OCR-based data extraction, and hospital-centered interoperability.

AMR Project for Ukraine

An AI-powered national health infrastructure project designed to build antimicrobial resistance (AMR) surveillance and response capacity in a healthcare environment severely disrupted by war. The project aims to standardize fragmented hospital, laboratory, and regional data and establish a sustainable foundation for real-time AMR monitoring, laboratory capacity strengthening, antimicrobial stewardship, and post-war health system recovery through AI and ontology-driven infrastructure.

AMR Project for Ukraine

An AI-powered national health infrastructure project designed to build antimicrobial resistance (AMR) surveillance and response capacity in a healthcare environment severely disrupted by war. The project aims to standardize fragmented hospital, laboratory, and regional data and establish a sustainable foundation for real-time AMR monitoring, laboratory capacity strengthening, antimicrobial stewardship, and post-war health system recovery through AI and ontology-driven infrastructure.

AMR Project for Ukraine

An AI-powered national health infrastructure project designed to build antimicrobial resistance (AMR) surveillance and response capacity in a healthcare environment severely disrupted by war. The project aims to standardize fragmented hospital, laboratory, and regional data and establish a sustainable foundation for real-time AMR monitoring, laboratory capacity strengthening, antimicrobial stewardship, and post-war health system recovery through AI and ontology-driven infrastructure.

Transforming a Traditional Beauty E-commerce Business with AI-ERP

Discover how a beauty e-commerce startup achieved 9x revenue growth and 75% workforce reduction by implementing CartaNova’s AI-ERP.

Transforming a Traditional Beauty E-commerce Business with AI-ERP

Discover how a beauty e-commerce startup achieved 9x revenue growth and 75% workforce reduction by implementing CartaNova’s AI-ERP.

Transforming a Traditional Beauty E-commerce Business with AI-ERP

Discover how a beauty e-commerce startup achieved 9x revenue growth and 75% workforce reduction by implementing CartaNova’s AI-ERP.

[ NEWS ]

Latest News

Comparison

See how we compare against others

See how we compare against others

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

Agile, custom solution development

Agile, custom solution development

Selective but deep, long-term partnerships

Selective but deep, long-term partnerships

Others

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

Large-scale, general-purpose solution development

Large-scale, general-purpose solution development

Broad but shallow, consulting-driven relationships

Broad but shallow, consulting-driven relationships

[ CONTACT US ]

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CartaNova, Inc.

Seoul.

3F, 8, Samseong-ro 118-gil, Gangnam-gu, Seoul, Republic of Korea

Tokyo.

7F, The Argyle Aoyama, 2-14-4 Kitaaoyama, Minato-ku, Tokyo 107-0061, Japan

R&D Center.

A-1418, 301, Incheon tower-daero, Yeonsu-gu, Incheon, Republic of Korea

© 2026 CartaNova, Inc. All Rights Reserved.

CartaNova, Inc.

Seoul.

3F, 8, Samseong-ro 118-gil, Gangnam-gu, Seoul, Republic of Korea

Tokyo.

7F, The Argyle Aoyama, 2-14-4 Kitaaoyama, Minato-ku, Tokyo 107-0061, Japan

R&D Center.

A-1418, 301, Incheon tower-daero, Yeonsu-gu, Incheon, Republic of Korea

© 2026 CartaNova, Inc. All Rights Reserved.

CartaNova, Inc.

Seoul.

3F, 8, Samseong-ro 118-gil, Gangnam-gu, Seoul, Republic of Korea

Tokyo.

7F, The Argyle Aoyama, 2-14-4 Kitaaoyama, Minato-ku, Tokyo 107-0061, Japan

R&D Center.

A-1418, 301, Incheon tower-daero, Yeonsu-gu, Incheon, Republic of Korea

© 2025 CartaNova, Inc. All Rights Reserved.