Companies in the US spend $15bn annually on talent acquisition. The most important metric in recruitment advertising is the conversion from the paid click on the job platform to the application the employer receives. Industry-wide, apply conversion is just 5%. Redirection of the candidate from the job platform to the company site is the biggest cause of abandonment; this step has a 70% bounce rate. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Mikhil Raja, Cofounder and CEO of SonicJobs, about how they have built AI Agents to enable candidates to complete applications directly on job platforms, without redirection, boosting completion rates to 26% from 5%. Raja delves deep into SonicJobs’ cutting-edge technology, which merges traditional AI with large language models (LLMs) to understand and interact with job application web flows. He also emphasizes the importance of fine-tuning foundational models to achieve more impactful and scalable innovations.
SonicJobs is a member of the NVIDIA Inception program for startups.
Time Stamps
1:19: Why applying for a job remains a Web 1.0 experience — and how SonicJobs’ AI Agents are changing this
6:06: Explanation of SonicJobs’ technology and the benefits to users and companies
9:03: The evolution of AI Agents from AutoGPT to Verticalized B2B solutions
11:33: How SonicJobs realized the approach it should take with Agentic AI
15:18: Scaling SonicJobs’ AI Agent and the adaptive learning flywheel
17:45: Raja discusses the need for accuracy including fine-tuning foundational models
20:45: Framework for how SonicJobs’ Verticalized AI Agent solution can be applied to further Verticals
23:23: Advice Raja would give to a company that’s currently trying to hire
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