Analysis
Apate, a Sydney-based startup, deploys AI agents that pose as potential victims -- answering scam calls, replying to phishing messages, and joining suspicious social media groups -- specifically to draw out fraudsters and extract details like bank accounts, crypto wallets, phone numbers and impersonated brand names, according to TechCrunch. The company then sells that intelligence to banks, telecom companies, government agencies and utilities, who use it for anti-fraud work, and customers can assign agents specific missions.
Founded in 2024 out of research at Macquarie University in Sydney, Apate is led by founder and CEO Dali Kaafar, with co-founder and chief commercial officer Brad Joffe. The company raised $8.15 million in seed funding in August from Lobby Capital, OIF Ventures, Investible, Concept Ventures and Baobab Ventures, bringing its total raised to just under $10 million -- a modest base relative to the compute and engineering cost of running agent conversations against live scammers at scale.
“Founded in 2024 out of research at Macquarie University in Sydney, Apate is led by founder and CEO Dali Kaafar, with co-founder and chief commercial officer Brad Joffe.”
Apate's approach differs from most fraud-intelligence competitors by design. Companies like Sift and Feedzai build models that detect fraud passively, scoring transactions or flagging anomalies after the fact, without ever engaging directly with a scammer. Apate's model requires sustaining a live conversation long enough to extract actionable details, closer in structure to a red-teaming or honeypot operation than a typical SaaS detection pipeline -- a difference that should show up in cost per engagement, not just in the marketing pitch.
The origin story is unusually concrete for a fraud-tech startup: Kaafar has said the idea came from keeping a scam call going for 44 minutes at a family picnic, extracting details the caller didn't intend to share. That kind of direct, repeated exposure to how scammers actually operate -- rather than working purely from aggregated fraud-loss statistics -- is the kind of product insight that's hard to fake and hard for a larger incumbent to replicate quickly.
Apate's customer base -- banks, telecoms, government agencies and utilities -- tends to move slowly through procurement and security review, which cuts both ways: slower initial sales cycles, but higher switching costs and better retention once a vendor clears the bar. With under $10 million raised, the near-term test is whether Apate can land a handful of named enterprise customers before it needs to go back to the market for a larger round to fund the compute-intensive scale-up its model requires.
