
AI Storage & Agent Ops Platform
for Enterprise RAG
AI specialist company providing next-generation AI system technology
through vertical integration and optimization of hardware and software.

Why Choose
Seahorse?
Seahorse Cloud delivers an end-to-end RAG platform with AI-powered storage that automatically parses, chunks, and vectorizes your documents using VLM, OCR, and LLM technologies.
Explore ProductAI-Powered Storage
S3-compatible Object Storage with autonomous document parsing
- VLM + OCR + LLM agentic parser
- Semantic chunking & auto vectorization

MCP Agent
Cross-platform AI agent with context memory and tool integration
- MCP standard tool calling
- Cross-session context memory

Transform Unstructured Data
into AI-Ready Knowledge
From AI-powered storage with autonomous document parsing to MCP-compliant agents, Seahorse delivers the complete RAG infrastructure with automatic vectorization pipeline.
Seahorse Cloud Platform
Next-generation AI-ready data platform with integrated vector intelligence.

Seahorse Database
World's fastest vector database with NVMe optimization and VDPU acceleration.



AI-Powered Storage
S3-compatible storage with autonomous VLM+OCR+LLM document parser and semantic chunking.

Seahorse Agent
MCP standard cross-platform agent
with context memory.


Latest News
and Achievements
CB Insights AI 100 2025
Dnotitia named to CB Insights' prestigious AI 100 list, recognized as one of the world's most innovative AI startups in the vector database infrastructure category.
₩16B Vector DB R&D Project
Secured ₩16 billion government-backed projects to develop high-performance vector database for LLM and the world's first VDPU semiconductor chip for vector data processing.
Seahorse Cloud 2.0 with AgentOps
Launched enterprise AI agent platform featuring AgentOps — enabling businesses to design and operate customized AI agents with LLM, vector database, and MCP tool integration.
₩3.7B AI Storage Development
Selected as lead organization for next-gen AI storage development with FADU and NHN Cloud. Target: 400B vectors/sec processing and 5,500MB/s random write performance.