Firms within the US spend $15bn yearly on expertise acquisition. A very powerful metric in recruitment promoting is the conversion from the paid click on on the job platform to the appliance the employer receives. Trade-wide, apply conversion is simply 5%. Redirection of the candidate from the job platform to the corporate website is the largest explanation for abandonment; this step has a 70% bounce fee. On this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Mikhil Raja, Cofounder and CEO of SonicJobs, about how they’ve constructed AI Brokers to allow candidates to finish purposes straight on job platforms, with out redirection, boosting completion charges to 26% from 5%. Raja delves deep into SonicJobs’ cutting-edge know-how, which merges conventional AI with giant language fashions (LLMs) to grasp and work together with job software internet flows. He additionally emphasizes the significance of fine-tuning foundational fashions to realize extra impactful and scalable improvements.
SonicJobs is a member of the NVIDIA Inception program for startups.
Time Stamps
1:19: Why making use of for a job stays a Net 1.0 expertise — and the way SonicJobs’ AI Brokers are altering this
6:06: Rationalization of SonicJobs’ know-how and the advantages to customers and corporations
9:03: The evolution of AI Brokers from AutoGPT to Verticalized B2B options
11:33: How SonicJobs realized the method it ought to take with Agentic AI
15:18: Scaling SonicJobs’ AI Agent and the adaptive studying flywheel
17:45: Raja discusses the necessity for accuracy together with fine-tuning foundational fashions
20:45: Framework for a way SonicJobs’ Verticalized AI Agent resolution might be utilized to additional Verticals
23:23: Recommendation Raja would give to an organization that’s at the moment making an attempt to rent
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