Cortea automates audit workflows using retrieval systems, data pipelines, and AI agents built on React, TypeScript, Python, and Temporal. The stack reveals a product designed around document ingestion and ranking (Elasticsearch, BigQuery, Spark, Ray) — addressing the core pain point of manual, document-heavy audits. A distributed engineering team with three lead-level roles and concurrent hiring in design and ops suggests a young company scaling product-market fit rather than still-searching.
Notable leadership hires: Design Lead, Chief of Staff
Cortea builds AI automation software for audit workflows, targeting teams that currently rely on manual processes and spreadsheet-driven reviews. The product surfaces retrieval-and-ranking systems to handle unstructured audit data, paired with data pipelines for ingestion and classification. Founded in 2024 and headquartered in Berlin, the company operates a 11–50-person team with engineering-forward architecture. Active projects span both product (audit flow experience, pricing models) and technical foundation (component libraries, architecture tooling), indicating movement from MVP toward scalable platform.
Frontend: React, TypeScript. Backend: Python, PostgreSQL, Temporal. Data layer: Elasticsearch, BigQuery, Snowflake, Apache Spark, Ray. Design: Figma.
Core projects: retrieval and ranking systems for AI agents, audit flow product experience, data pipelines for ingestion and classification. Infrastructure: architecture tooling, component libraries, and employer branding for engineers.
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