AI Lab Members
The people who research AI and build it for real
The researchers and engineers who make Plateer AI Labs. From research through product development to customer deployment, they turn enterprise AI into something real — and share what they learn along the way.
Leadership
남덕현
CTO · Head of Labs
정우문
AI R&D Lead · CTO Office
최수안
Product Strategy Lead
2 tech notes
손성준
Product Technology & Consulting Lead
장하렴
Product Engineering Lead
최종민
Architecture Engineering Lead
Architecture Engineering
박예원
Architecture Engineering
채희철
Architecture Engineering
전인수
Architecture Engineering
AI R&D
이다운
AI R&D
김해수
AI R&D
김동욱
AI R&D
김대희
AI R&D
김진수
AI R&D
20 tech notes
유지수
AI R&D
5 tech notes
박소민
AI R&D
권오영
AI R&D
박태준
AI R&D
What they build
Our research does not stay in papers. It ships as open-source libraries and projects, public to anyone on the Plateer AI Labs GitHub organization.
XGEN Platform
The execution layer that puts agents to work — runtime, execution harness, and backend SDK, all open source.
Ontology & Knowledge Engine
Turning enterprise documents and data into knowledge an agent can reason over — knowledge graphs, GraphRAG, and agent memory.
Document & Data Ingestion
Making the formats enterprises actually have readable and writable by AI — 80+ document parsers, chunking, and Office document editing.
Agent Tooling
The tools where agents meet the outside world — search, browser control, and tool-definition linting.
What they publish
New capabilities as they take shape, what building them taught us, and what we learned in customer environments.
XGEN Preview
A first look at what we are building — what it does, why we are building it, and how it is used.
See previewsTech Notes
Problems we hit building ontology, RAG, and agent runtimes — and how we worked through them.
Read tech notesField Reports
The questions that actually come up when enterprises evaluate AI platforms, and our answers to them.
Read field reports