From scam delays to operational intelligence
Apate.ai said in its funding announcement that it raised US$8.15 million in seed funding led by Lobby Capital. OIF Ventures, Investible, Concept Ventures and Baobab Ventures also participated, bringing the company’s total funding to just under US$10 million.
Founded in 2024 from research conducted at Macquarie University, Apate is expanding in North America and Europe with what it describes as an autonomous fraud counterintelligence platform. The company’s commercial proposition is straightforward: instead of waiting for a scam campaign to produce victims, organizations can send AI agents into the conversations while the fraud is still underway.
TechCrunch reported that Apate has deployed thousands of agents to answer scam calls, respond to suspicious messages and emails, follow phishing links and enter social media groups. The agents use fabricated identities with different ages, genders, accents and languages, allowing them to appear more like the varied targets scammers expect to encounter.
The commercial value lies in what the agents can uncover. By keeping fraudsters engaged, they may extract bank account details, cryptocurrency wallet addresses, telephone numbers, malicious websites, impersonated brands and information about how campaigns are organized. Apate says its agents have accumulated 17 years of scammers’ time across 2.5 million conversations.
That activity creates a data layer that conventional fraud defenses often lack. Phone numbers, websites and payment accounts can change quickly. A conversation can reveal the relationships among those assets, the scripts used by a campaign and the next step a criminal expects a victim to take.
A platform built around missions
Apate is not presenting the agents merely as automated prank callers. The company’s product materials describe three parts of the platform: Apate Voice, Insights and Text.
The agents can engage scammers across telephone and messaging channels using multilingual victim personas. Customers can assign specific missions, such as finding a cryptocurrency wallet, identifying a bank account or collecting evidence about a particular fraud campaign. The resulting exchanges are converted into real time intelligence through the company’s Insights product.
That mission based model is strategically important. It gives banks and other customers a way to direct the system toward information with immediate operational value, rather than purchasing a general archive of scam conversations. In theory, a telecommunications provider might seek numbers linked to an impersonation campaign, while a bank could prioritize payment accounts or transfer instructions.
Apate’s use case material positions the platform for telecommunications companies, banks and government agencies. It describes applications including campaign mapping and intelligence extraction, and reports a case study in which 300,000 calls were passed to Apate each month, diverting 613 hours of scammer talk time.
The company’s reported customers include Commonwealth Bank of Australia and TPG Telecom. Apate says TPG diverted nearly 590,000 calls to its agents, while Commonwealth recorded a 76% decline in publicly reported scam losses. Apate also says another Australian bank used outbound agents to disrupt 22 scam campaigns.
Those figures are company reported outcomes, but they illustrate the business model’s intended advantage. Apate can sell not only software access, but also a continuously updated view of how fraud networks operate. That could make its data more valuable over time, particularly if customers contribute new targets and use the platform’s findings to improve detection and disruption.
Growth brings unresolved risks
Apate combines frontier models with fine tuned models trained on scam conversations and earlier university research. Its funding will support further product development and international expansion, including operations or offices in London and the United States.
The expansion also increases the importance of safeguards. Automated deception may create useful intelligence, but the system must distinguish criminal activity from legitimate contact, avoid interfering with people acting under coercion and prevent sensitive information from being mishandled.
Only one critical outside view was found for this report. The Guardian reported that Richard Buckland, a cybercrime professor at the University of New South Wales, supported the honeypot concept but warned about misidentifying legitimate callers, targeting people working under coercion and giving criminals material they could use to train their own systems. Australia’s National Anti-Scam Centre said it wanted to review evaluations of Apate.
That means Apate’s competitive advantage will depend on more than model quality. It will need reliable targeting, evidence that its intelligence leads to measurable reductions in losses, and controls that make its deception acceptable to regulators and commercial customers. If it succeeds, the startup could establish a new category in fraud prevention: not just detecting scams, but actively occupying the conversational layer where scam networks coordinate and recruit victims.
Rebeca Smith is not a person. No notebook, no deadlines, no face behind the name — just a byline this newsroom publishes under. Here is the production line underneath it, because a name beside a portrait reads like a journalist, and this one is not one.
The models. Writing: gpt-5.6-luna and qwen3-max. Out on the live web: gpt-5.6-luna and gpt-5.6-terra. Pictures: gpt-image-1 and gpt-image-1-mini. Swap one in the newsroom and this line swaps with it — it is read off the machines, not typed here.
How a story is made
- Research. The searching model reads around the story, pointed at primary sources — the filing, the post, the repository — rather than at somebody else's write-up of them.
- Writing. The writing model drafts it against what was found, at Rebeca Smith's usual length and in Rebeca Smith's usual register.
- The loop. A reviewer reads the draft and sends it back with notes. Then reads it again. A piece can go round several times before it leaves the building.
- Enrichment. A quotation has to appear word for word on the page it is taken from. A chart may only use figures that appear in the source it cites. Whatever fails is dropped, and the reason is kept.
- Fact check. A last pass hunts for claims the article makes and its sources do not.
- A human stop. Sensitive subjects are held for a person to read before publication, and a person can kill any of it at any point.
If that sounds less like a newsroom and more like a factory: quite. It is called Press Factory.
This article was generated using AI and published automatically without human pre-publication review.
How this article was made
The article was produced by the Grandmonts Media News Engine using automated research, drafting and verification workflows. No human editor reviewed the article before publication. Grandmonts Media remains responsible for the published content. Errors can be reported at office@grandmonts.cz.