Lucis uses LangChain, Claude, and a multi-agent AI system to translate blood-test data into personalized longevity insights. The stack reveals a company building both inference infrastructure (vector/RAG, LLM endpoint reliability) and go-to-market velocity (Braze, Klaviyo, AppsFlyer, paid-spend scaling to €150k+). Hiring is balanced between marketing (3), engineering (2), and leadership roles, suggesting a product-market fit phase where clinical credibility and user acquisition are moving in parallel.
Lucis is a Paris-based longevity platform that interprets blood biomarkers through AI to help users understand their health trajectory and lifespan projections. The product ingests lab data, normalizes it through pipelines, and applies clinical science algorithms powered by LLMs to generate personalized insights. The company operates under HDS and GDPR compliance frameworks. Current focus areas include scaling paid acquisition across European markets (UK launch underway at €90 CAC target), maintaining production reliability of LLM endpoints, and handling 10× load growth on infrastructure.
Lucis uses AWS, Terraform, Python, LangChain, Claude, and ChatGPT for core product. For go-to-market: Braze, Klaviyo, AppsFlyer, and Intercom. Data and collaboration tools include Airtable, Notion, and Figma.
Multi-agent AI doctor system, vector/RAG architecture, HDS-compliant infrastructure scaling, clinical algorithm translation, and geographic expansion (UK market launch). Also scaling paid acquisition spend while managing customer acquisition cost.