5 Ways to Improve Your ML System
5 Ways to Improve Your ML System | Reduce cost and improve the quality of your Machine Learning pipelines.
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Case Study: Rebuilding a Scalable Student Financing Platform with Next.js and Prisma
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
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Case study: AI Legal Risk Detection | LLM + RAG for Content Review
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
Case study: AI Legal Risk Detection | LLM + RAG for Content Review Read More »
AI-Powered Sales Intelligence – Detecting Tech Buying Signals Through Job Postings
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
AI-Powered Sales Intelligence – Detecting Tech Buying Signals Through Job Postings Read More »
Case study: reducing 65% of ML pipeline time at Google [code available]
The AI Flow CEO helped Google push the boundaries of feature selection in machine learning pipelines. By building on top of their state-of-the-art research, we developed a tool that reduces feature sets by up to 64%, while maintaining model performance—cutting training time, inference time, and model size significantly, with the potential to scale across Google’s ecosystem.
Case study: reducing 65% of ML pipeline time at Google [code available] Read More »