Compliance, reimagined
Financial institutions spend significant time on alerts, evidence gathering, case documentation, and SAR preparation. Often across fragmented systems. Argus is an Agentic compliance platform built to streamline that path from investigation to filed report.
The problem we're solving
Financial institutions spend significant time investigating alerts, gathering supporting evidence, validating findings, documenting cases, and preparing Suspicious Activity Reports (SARs). These workflows are often fragmented across systems, creating delays, inconsistency, and audit challenges.
How Argus works
Multiple agents work in sequence with human approval at every gate. The Risk Analyst Agent profiles customers, detects suspicious patterns, and scores each case with explainable reasoning. After your team approves, the Compliance Officer Agent drafts the complete SAR—regulatory citations, structured sections, and a narrative aligned to the Five Ws. Nothing is filed automatically.
The workflow
Data Ingestion → Risk Analyst Agent → Human Review → Compliance Officer Agent → Human Review → SAR/DS Generation + Audit Log
Key capabilities
- Explainable AI reasoning chains
- Human approval gates
- Jurisdiction-aware compliance rules
- Automated SAR narrative generation
- Complete audit traceability
- Multi-jurisdiction support (FinCEN, EU AMLD6, UK MLR)
Supported jurisdictions: FinCEN US, EU AMLD6, UK MLR, MENA (coming soon).
Why it matters
Manual SAR preparation can cost hundreds to thousands of dollars per case. Argus reduces investigation and documentation effort while improving consistency, defensibility, and compliance readiness.
Where we are
Argus is an early stage product built by a small team. We're working with our first compliance professionals to validate the approach and improve output quality. If you work in AML compliance and want to give honest feedback, we'd love to hear from you.
Want to talk?
We're always open to conversations with compliance professionals, fintech founders, and anyone who thinks this problem is worth solving.
Get in touch