S

Skelter Labs Tech Stack

Conversational AI and LLM application platform for enterprises

Information Technology & Services Seoul, Seoul 51–200 employees Founded 2015 Privately Held

Skelter Labs builds conversational and speech AI systems for Korean enterprises, with a product suite spanning Q&A chatbots, on-premises language models, task-oriented chat builders, and speech recognition/synthesis. The stack centers on Python, LangChain, LlamaIndex, and RAG—confirming a focus on retrieval-augmented generation and LLM orchestration. Pain points cluster around hallucination reduction, AI safety, and data quality, signaling that the company is navigating real production challenges in deploying large models to risk-averse enterprise customers.

Tech Stack 18 technologies

Core StackReact Vue AWS Docker Kubernetes MySQL PostgreSQL MongoDB Redis RAG Jira Confluence Python LangChain Linux GCP Git LlamaIndex

What Skelter Labs Is Building

◆Challenges

Reducing hallucinations
Bridging business-technical gap
Solving customer business problems
Risk management of ai agents
Data quality for training
Ensuring ai safety
Managing ai risk

▲Active Projects

Ai agent build project
Ops
Llm-as-a-judge evaluation pipeline
Rag-based search accuracy improvement
Red teaming training initiative
Aiq+ product implementation

Hiring Activity

Steady7 roles · 2 in 30d

Department

Engineering

5

Product

1

Seniority

Senior

4

Mid

2

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About Skelter Labs

Skelter Labs develops conversational AI solutions for enterprise deployment, founded in 2015 and headquartered in Seoul. The product line includes BELLA QNA (enterprise data + LLM Q&A chatbot), BELLA LLM (on-premises custom language model), AIQ+ Chat (task-oriented LLM builder), AIQ+ Speech (speech-to-text and text-to-speech), and AIQ+ Answer (document-aware search assistant). The company operates at 51–200 employees with an engineering-heavy hiring posture focused on senior and mid-level roles, primarily recruiting within South Korea. Skelter Labs positions itself around custom integration of large language models into enterprise workflows, emphasizing data control and safety.

HeadquartersSeoul, Seoul

Company Size51–200 employees

Founded2015

Hiring MarketsSouth Korea

Frequently Asked Questions

What tech stack does Skelter Labs use?

Skelter Labs uses Python, LangChain, LlamaIndex, and RAG for LLM development; React and Vue for frontends; AWS and GCP for infrastructure; Docker and Kubernetes for deployment; MySQL, PostgreSQL, and MongoDB for persistence; and Redis for caching. Git, Jira, and Confluence support operational tooling.

What products does Skelter Labs offer?

Skelter Labs offers BELLA QNA (Q&A chatbot with LLM + enterprise data), BELLA LLM (on-premises custom LLM), AIQ+ Chat (task-oriented LLM chatbot builder), AIQ+ Speech (STT/TTS solutions), and AIQ+ Answer (document-aware search assistant). Current focus includes RAG-based search improvements and AI agent evaluation pipelines.