Firms within the US spend $15bn yearly on expertise acquisition. Crucial metric in recruitment promoting is the conversion from the paid click on on the job platform to the applying the employer receives. Trade-wide, apply conversion is simply 5%. Redirection of the candidate from the job platform to the corporate web site is the largest reason for abandonment; this step has a 70% bounce charge. 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 functions instantly 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 know and work together with job software net flows. He additionally emphasizes the significance of fine-tuning foundational fashions to attain 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: Clarification 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 strategy 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 could be utilized to additional Verticals
23:23: Recommendation Raja would give to an organization that’s presently making an attempt to rent
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