AI & Machine Learning Company in Seoul

Three delivery shapes drive the range: a scoped AI proof of concept at Seoul rates, a custom production ML model (chaebol-scale demand forecasting, financial fraud scoring, Korean document extraction with KoNLPy and Mecab-ko preprocessing), or full production AI systems with Korean LLM fine-tuning, multi-model RAG across HyperCLOVA X and EXAONE, on-device Galaxy or Hyundai deployment, and full enterprise integration. Scope drivers include a Korean-language data audit, a baseline Korean LLM or classical ML model, and a hosted demo on AWS ap-northeast-2 or Naver Cloud Platform. Seoul rates sit above Bangkok and Ho Chi Minh City because of the chaebol and Naver compensation benchmark, but below Tokyo and Singapore. We give fixed-fee proposals so finance and procurement at Samsung, LG, and the major banks can sign without an open-ended T&E exposure.

Korea's AI Framework Act, formally the Basic Act on the Development of Artificial Intelligence and Establishment of a Trust Foundation, was passed by the National Assembly in December 2024 and is one of Asia's first comprehensive AI laws alongside the EU AI Act and India's evolving IndiaAI framework. The Act introduces obligations around high-impact AI in employment, credit, healthcare, biometrics, public services, and critical infrastructure, including risk management, transparency, human oversight, post-market monitoring, and provider-versus-deployer responsibility allocation, with phased applicability through 2025 and 2026 supported by IITP and MSIT guidance. Our discovery phase runs the classification on your specific use case, and high-impact builds ship with a Korean-language model card, an algorithmic impact assessment, bias and fairness review, and a monitoring plan suitable for IITP and PIPC scrutiny. Even before the implementing regulations finalise, Korean enterprises and public buyers are already requiring Framework-Act-style governance because the policy direction has been clearly telegraphed.

Yes. We have working integrations with Naver HyperCLOVA X through Naver Cloud Platform, LG EXAONE 3.5 through LG AI's enterprise endpoints, Kakao KoGPT, Upstage SOLAR including the 10.7B instruction-tuned variants, and the KORani family for community-grade Korean LLM work. For fine-tuning we run LoRA and QLoRA on H100 or A100 instances inside ap-northeast-2 or Naver Cloud, with Korean morpheme-aware tokenisation through Mecab-ko or Khaiii so the model does not waste capacity on agglutinative noise. Retrieval-augmented generation is built with Korean-language embeddings (KoSimCSE, BGE-M3 with Korean fine-tuning, or in-house contrastive training on client corpora) and Korean-aware chunking that respects eojeol and sentence boundaries rather than naively splitting on whitespace. For chaebol buyers we route through Naver Cloud or LG endpoints where sovereign Korean inference is a procurement requirement, and we self-host open-weight Korean models when neither hosted option meets the cost or residency profile.

The Personal Information Protection Act (PIPA) is one of the strictest data protection regimes in Asia, enforced by the Personal Information Protection Commission (PIPC) and supported by KISA on technical guidance. Our PIPA discovery produces a Korean-language data flow map, a written processing inventory, lawful basis documentation for every purpose, explicit consent flows where consent is the chosen basis, and clear separation between essential and optional consent that PIPC has fined companies for collapsing. Cross-border transfers are routed through PIPA-compliant mechanisms with the recipient country's adequacy status documented, and US-hosted model providers are evaluated against PIPC's tightening line on overseas LLM inference. Pseudonymisation and de-identification for AI training data follow PIPC's technical guidance, with re-identification risk assessments and access controls aligned with KISA's ISMS-P framework where the client carries that certification. Breach notification is designed against PIPA's 72-hour window with templates pre-reviewed by the client's CPO.

When a project requires genuine research depth (novel architectures, multilingual Korean-English-Chinese-Japanese transfer learning, rare-event detection in industrial vision, reinforcement learning at chaebol scale), we scope collaborations with KAIST faculty in Daejeon, Seoul National University labs, POSTECH researchers in Pohang, Yonsei, or Korea University rather than overselling in-house capability. Our core team handles applied engineering, MLOps, Korean-language production deployment, and integration into Naver Cloud, AWS Seoul, or chaebol internal platforms, which is where most Seoul AI projects actually stall. For standard work (Korean RAG, HyperCLOVA X or EXAONE integration, classical ML, computer vision on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits into, and we have introduced Seoul clients into IITP and MSIT funded consortium programs when those subsidies meaningfully change the unit economics of the build.

We default to AWS ap-northeast-2 in Seoul, Azure Korea Central in Seoul, and GCP asia-northeast3 in Seoul for storage, training, and inference. For chaebol divisions and public sector buyers who require Korean sovereign cloud we deploy on Naver Cloud Platform (NCP), which hosts HyperCLOVA X and is the credible domestic alternative to US hyperscalers. For LLM features that must stay in Korea (PIPA-sensitive consumer data, financial data under FSC supervision, Samsung or LG internal information, Hyundai vehicle telemetry) we use HyperCLOVA X on NCP, EXAONE 3.5 on LG endpoints, or self-hosted SOLAR, KORani, Llama 3, and Qwen 2.5 on Korean GPU instances. Cross-border inference (Anthropic, OpenAI, Google) is permitted only when a PIPA cross-border assessment signs off, and we document the residency choice in the Korean-language model card so PIPC reviewers and chaebol legal affairs teams have an unambiguous answer.

On-device AI inside Galaxy hardware or Hyundai vehicle compute typically takes eighteen to twenty-eight weeks from kickoff to production-ready build, longer than equivalent server-side AI because the engineering surface is different. Weeks one to four are model architecture selection, quantisation strategy (INT8 or INT4), and target-device profiling against Samsung Exynos NPU, Qualcomm Hexagon, or Hyundai vehicle compute constraints. Weeks five to twelve cover model training, distillation from a larger teacher, and aggressive size reduction through pruning and quantisation-aware training. Weeks thirteen to twenty wire up the on-device inference runtime (TensorFlow Lite, ONNX Runtime, ExecuTorch, Samsung One Embedded SDK, or vehicle-specific stacks) with battery, thermal, and memory budgets respected. Weeks twenty-one onward run chaebol internal QA, security review, and certification gates. Galaxy AI patterns since the S24 series and Hyundai Pleos infotainment work have made this category more predictable but still demanding.

Yes. Selling AI into Samsung, LG, Hyundai, SK, or KT is not a quarter-by-quarter motion. The buying cycle typically runs twelve to eighteen months through technical due diligence, security review, legal affairs (beopmu), procurement (gumae), and division-level executive sponsorship, often coordinated through a Korean partner or system integrator like Samsung SDS, LG CNS, or SK C&C. We structure the technical engagement to survive that cycle by producing Korean-language architecture documents, security questionnaires aligned with chaebol internal frameworks, a proof of concept that can be hardened into production rather than thrown away, and a build sequence that allows incremental approval rather than a single end-of-project gate. We coordinate with the client's hyeopryeob (cooperation) and gaebal (development) teams in their preferred tools and cadence, and we accept that Korean enterprise procurement runs on relationships and document trails, not on demo theatrics.