Moo-Kyoung Chung
CEO
Dnotitia
10/10Traction
Dnotitia provides a dual‑product platform for enterprise AI: Seahorse™ is a cloud‑native vector database powered by proprietary VDPU hardware that delivers ultra‑fast, high‑accuracy semantic search across multimodal unstructured data, while Mnemos™ is an edge device that runs optimized, compressed large language models locally without cloud infrastructure. Together, the solutions enable rapid indexing, retrieval, and inference on large datasets with low latency, reduced total cost of ownership, and enhanced data security.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Enterprise Software
- Hardware
- Software Only
Updated 4 months ago
Funding
Raised to date
$14.6MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BonAngels Venture PartnersNo round attributed
- bonangels.net (opens in a new tab)investor website
HbvcNo round attributed
- hbvc.co.kr (opens in a new tab)investor website
Funding rounds are not available yet.
Founders
Product
Problem
Enterprises face growing volumes of unstructured data that are difficult to index, search, and retrieve efficiently, leading to high storage costs and slow insight generation. Deploying large language models (LLMs) typically requires cloud data centers, which adds latency, increases operational expenses, and raises data‑security concerns for edge or on‑premise use cases.
Solution
Dnotitia offers a dual‑product platform that addresses both challenges. Seahorse™ is a cloud‑native vector database built on proprietary Vector Data Processing Units (VDPU) that delivers ultra‑fast, high‑accuracy semantic search across multimodal data types, with automated pipeline design, auto‑scaling, and multi‑cloud integration to reduce total cost of ownership. Mnemos™ is a standalone edge device that runs optimized, compressed LLMs locally, eliminating the need for cloud infrastructure while providing low‑power, high‑performance inference and automatic model tuning. Together, the solutions enable organizations to ingest, index, and retrieve unstructured data at scale and to run advanced AI applications securely at the edge.
Target Audience
Primary customers are enterprises that need rapid semantic search over large unstructured datasets—such as media, finance, and healthcare firms—and organizations requiring on‑premise or edge AI capabilities, including smart factories, government agencies, and medical device manufacturers.
Features
- VDPU‑accelerated vector database delivering up to 10× faster query performance and 80% lower TCO compared to traditional databases
- No‑code RAG pipeline builder with visual design tools for end‑to‑end data ingestion, embedding, and model tuning
- Multi‑cloud and diverse storage integration with containerized microservices and Kubernetes orchestration
- Adaptive indexing and multimodal optimization supporting text, images, time‑series, and geospatial data
- Edge LLM device featuring proprietary LLM compression, compatible SLM foundation models, and automatic on‑device tuning
- Low‑power hardware architecture enabling high‑performance inference without GPU/NPU clusters
- Enterprise‑grade security with encryption at rest and in transit, granular access controls, and SSO integration