AI-powered personal finance platform combining chat, budgeting, and credit products
Cleo is a consumer fintech built around an AI assistant that helps users manage money through conversational chat. The stack reflects a mature product organization: Python + PostgreSQL + Kubernetes on AWS for backend infrastructure, React Native + React for client-side, and integrated LLM calls (OpenAI, Anthropic). Active projects span secured credit products, payment optimization, and financial health coaching—signaling a shift from pure budgeting software toward a fuller suite of lending and transaction services. Hiring is accelerating across engineering and product (55 roles posted in the last 30 days), with seniority distribution skewed toward senior IC and leadership roles, indicating scaling of complexity rather than headcount-only growth.
Notable leadership hires: Tech Lead, InfoSec Director
Cleo operates a mobile-first AI assistant for personal finance management, launched in 2016 and headquartered in London. The platform combines chat-based financial guidance, budgeting tools, transaction analysis, and credit products (including a secured credit card now in active development). The user base spans millions of active consumers, primarily in the UK and US. The tech footprint includes Python/Ruby on Rails backends with PostgreSQL, React Native mobile apps, and analytics pipelines using dbt, Airflow, and Looker. Recent growth has expanded the team from ~100 to 250+ employees; the company closed an $80M Series C at a $500M valuation. Current engineering focus includes payments platform optimization, chatbot quality improvement, and architectural migration from monolith to modular services.
Backend: Python, Ruby on Rails, PostgreSQL, Kubernetes, AWS, GCP. Mobile: React Native. Web: React, Next.js. Data: dbt, Apache Airflow, Looker, Tableau. AI: OpenAI, Anthropic integration. Deployment: CircleCI, Vercel, Heroku.
Active hiring in United Kingdom, United States, Portugal, and Germany. Headquarters in London, England.
Cleo'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.