AI-driven OSINT platform for threat detection and financial crime prevention
Fivecast builds AI-driven open-source intelligence (OSINT) tools for threat detection, fraud prevention, and financial crime investigations. The tech stack—Java, Python, TensorFlow, PyTorch, Elasticsearch, PostgreSQL on AWS/GCP—reflects a machine-learning-first architecture for real-time risk analytics. Current hiring is engineering-heavy (4 of 6 open roles), weighted toward senior and mid-level IC positions, suggesting they're scaling AI and backend capabilities faster than sales or product—aligned with their active work on AI-enabled prototype development and cloud-native feature architecture.
Fivecast is a privately held Australian software company (founded 2017, 51–200 employees, Adelaide HQ) that develops AI-driven risk analytics and OSINT platforms for law enforcement, financial institutions, and enterprises. The platform combines advanced open-source intelligence collection with machine learning models (TensorFlow, PyTorch, scikit-learn) to deliver real-time threat detection, fraud investigation, and financial crime prevention capabilities. Core offerings span social media intelligence (SOCMINT), digital footprint analysis, social network mapping, and investigative analytics. Go-to-market is supported by HubSpot and Salesforce, with ZoomInfo for lead generation. The company operates across Australia and the United States.
Java, Python, PostgreSQL, Elasticsearch, React, Angular, Spring, TensorFlow, PyTorch, scikit-learn, AWS, and GCP. Also uses HubSpot, Salesforce, ZoomInfo, and compliance tools (Vanta, Drata).
Yes. 4 of 6 active roles are engineering positions (3 senior, 1 mid-level). They are also hiring product (1 role) and sales (1 role) across Australia and the United States.
AI-driven prototype development for financial crime, scalable cloud-native AI features, advanced AI integration into enterprise software, event amplification, and outbound calling campaign automation.
Fivecast'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.