AI investment data platform for family offices and private banks
Landytech operates Sesame, an investment management platform built on Python, Java/Spring, React, and data-processing tools (Polars, DuckDB, Elasticsearch). The tech stack reflects dual investment in backend analytics (NumPy, Polars, DuckDB for data processing) and modern frontend infrastructure (React, TypeScript, Redux), with active work on ETL monitoring and automation. Current hiring is concentrated in data and sales roles alongside engineering, and the project list reveals pain around engineering dependency for ETL and early-stage development — typical for a fintech platform still maturing its data pipelines.
Landytech builds Sesame, an AI-powered investment management platform for family offices, trust companies, private banks, and asset managers across Europe. The product aggregates data from over 500 custodians and handles portfolio analytics, investment reporting, and bookkeeping automation. The company emphasizes security with ISO 27001 certification, SOC II compliance, and physically isolated client environments. Founded in 2018 and based in London, Landytech operates a high-touch B2B sales model and is actively expanding into new territories, alongside ongoing work on risk monitoring, exception handling, and private asset data management features.
Python, Java with Spring Boot, React and React Native on the frontend, Polars and DuckDB for data processing, Elasticsearch for search, Docker and Kubernetes for infrastructure, and Azure DevOps for CI/CD.
Sesame connects to over 500 custodians, making it Europe's most-connected investment management platform for the family office and private bank segment.
Landytech's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.