Doppel was built to defend organizations from deepfakes and online impersonations, but quickly realized AI meant threats could scale infinitely. Attackers no longer needed to handcraft scams; they could generate endless variants of phishing kits, spoofed domains, and impersonation accounts in seconds.

To stay ahead, Doppel developed a new kind of social engineering defense system built on OpenAI GPT‑5 and o4-mini models. Doppel’s platform detects, classifies, and takes down threats autonomously, cutting analyst workloads by 80%, triples threat-handling capacity, and reduces response times from hours to minutes.

Traditional digital risk protection relied on humans to manually review impersonation sites, phishing domains, and social media profiles and posts. Doppel saw that model breaking down as attackers began to automate, launching threats faster, and across more surface areas, than humans could evaluate them.

That speed is critical for Doppel’s customers, organizations that can’t afford to wait hours to confirm a threat. Doppel’s system classifies most threats automatically, using OpenAI models for reasoning and a structured feedback loop known as reinforcement fine-tuning (RFT) to improve the model over time. In RFT, human feedback is used as graded examples, helping models learn to make consistent, explainable decisions on their own.