성과
- Clean, reliable data flowing into AI systems automatically
- Infrastructure that scales without runaway cloud costs
- Faster iteration cycles for data science and ML teams
Data Engineering and AI Infrastructure
Data engineering is the number one bottleneck for enterprise AI. If your data is messy, siloed, or unavailable, no model will fix it. We build the data foundations — ETL pipelines, vector databases, feature stores, and cloud infrastructure — that make AI systems performant, cost-efficient, and scalable.
산출물
- Data architecture assessment and design
- Pipeline development (ETL/ELT, streaming, batch)
- Vector database and embedding infrastructure
- Cloud cost optimization and scaling plan
적합한 대상
- Companies whose AI projects are bottlenecked by data quality
- Teams scaling AI workloads and hitting infrastructure limits
- Organizations moving from proof-of-concept to production AI
관련 서비스
AI Strategy and Adoption Roadmaps
Turn broad AI ambition into a clear plan your founders, managers, operators, and delivery teams can follow.
자세히 보기 →AI Agents and Workflow Automation
Design agentic workflows that handle real steps across tools, approvals, and customer or employee requests.
자세히 보기 →LLM Knowledge Systems and Search
Build AI search and knowledge experiences that connect documents, product information, process notes, and internal expertise.
자세히 보기 →시작할 준비가 되셨나요?
프로젝트에 대해 알려주시면 귀사의 팀과 일정에 가장 적합한 접근 방식을 추천해 드리겠습니다.