Constellab는 월간 방문 0, 전월 대비 성장률 0%, 가격 문의 가격 모델을 기록합니다. Labnote는 월간 방문 676, 성장률 -91.4%, 가격 문의 가격 모델을 기록합니다.

Constellab is a sovereign European data and AI platform for life sciences that lets research and health teams build, automate and share analysis workflows inside secure, traceable digital labs.

Labnote Scholar is a bio and chemistry-specific AI research assistant that structures experimental data, predicts experiment outcomes, manages reagent and MSDS records, applies RAG search over internal papers and generates reports automatically. It is sold to R&D organisations with dedicated onboarding, GLP/GMP alignment and enterprise security.
| 항목 / 지표 | ![]() | ![]() |
|---|---|---|
| 공식 도메인 | constellab.io | labnote.co |
| 가격 모델 | 가격 문의 | 가격 문의 |
| 월간 방문 수 | 0 (0) | 676 (676) |
| 전월 대비 트래픽 변화량 | 0 (0%) | -7.17천 (-91.4%) |
| 주요 시장 및 점유율 | 🌐 글로벌 (—) | 🇰🇷 대한민국 (100%) |
| 월 직접 방문 수 | 0 | 584 |
| 사용자 평점 | 4.6 | 4.3 |
| 지원 플랫폼 | 웹사이트 | 웹사이트 |

Constellab is a sovereign European data and AI platform for life sciences that lets research and health teams build, automate and share analysis workflows inside secure, traceable digital labs.
Assemble analysis pipelines from reusable method bricks so scientific workflows are reproducible and easy to share across teams.
Bring together data from instruments, files and external sources into one governed, traceable environment.
Choose cloud, on-premises or edge deployment so sensitive life-sciences data stays where governance requires.
Manage research projects, search across datasets and run analyses from a unified workspace built for scientific teams.

Labnote Scholar is a bio and chemistry-specific AI research assistant that structures experimental data, predicts experiment outcomes, manages reagent and MSDS records, applies RAG search over internal papers and generates reports automatically. It is sold to R&D organisations with dedicated onboarding, GLP/GMP alignment and enterprise security.
Capture experiments in a structured electronic lab notebook with audit-trail support, so records satisfy regulatory documentation expectations rather than living in scattered files.
The platform applies AI to experimental design and outcome prediction, reducing trial-and-error cycles and cutting down duplicated experiments across a lab.
Track reagents and manage MSDS documentation in the same system as the experiment records that consume them.
Retrieval-augmented search runs against the organisation's own papers, reports and prior experiments, so answers are grounded in internal knowledge rather than the open web.

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