The paperwork burden shocked British doctor Ahmed Kerwan when he started working as a physician. On some days, he only spent three hours actually caring for patients, and spent the rest of his working days dealing with insurance claims and other things.
Dozens of startups have used AI to ease the infamous management burden in healthcare. From the notes expert Like to indent arrive The entrepreneurial environment of AI assistantsthese startups are working to simplify efficiency. Kerwan, now an entrepreneur, is the founder and CEO of one of the companies TAXO. His startup offers an application that doctors and others use for tasks such as obtaining prior authorization from insurance companies, patient intake and medical billing.
Kerwan said what sets Taxo apart is its AI “inference engine”, which transparently explains its process of making decisions about users, thereby helping it build trust with doctors. Inference Model Become the mainstream in the AI world at the end of last year Open logic to users through public. Compared with the industry Average According to Kerwan, about 80%.
Taxo builds its “inference engine” by adding healthcare-specific layers on top of Openai and Anthropic’s, such as Openai’s and Anthropic’s. It said the system is trained on inaccessible healthcare data, which makes it difficult for others to scratch overnight. “Every time Openai launches a new model, we don’t want to be suppressed,” Kerwan told TechCrunch.
AI’s inference trend is still early And only real traction The rise of Chinese startup DeepSeek. But investors’ interest in Taxo shows that the technology has an opportunity to gain wider adoption beyond the underlying AI companies. The startup recently concluded a $5 million seed round led by Y Combinator, General Catalyst and Charat Capital.
Founded last year and based in San Francisco, Taxo told TechCrunch IT passed a $1 million ARR six months after its launch. Now, it serves about 15 clients, from clinics to government providers.
Kerwan told TechCrunch that when Chatgpt was released, doctors were cautious about using it because they couldn’t track why and how to make specific suggestions. He hopes Taxo changes that. “You can see exactly what information we get and why it is provided,” he said.
