Mohit Rawat

The website presents a clear summary of Mohit Rawat’s career, highlighting fifteen years in AdTech and messaging systems. His current focus on building AI systems end-to-end is evident through his work at Galvor, where he developed an ad-creative intelligence platform using LLMs and multimodal embeddings. Previous roles at Bizense and Terragon add further context to his experience in customer data platforms and ad networks. The site functions primarily as a working log, offering short, unpolished entries on engineering principles and system notes. While the content is dense with technical detail, it effectively showcases his background in aerospace engineering and his ability to lead engineering teams across different regions. The inclusion of links to LinkedIn and GitHub provides additional avenues for verification. Overall, it serves as a straightforward portfolio for someone interested in his technical trajectory and recent projects in the AI space.

The daily log format makes it very easy to track his evolving thoughts on AI systems and engineering principles.

It is refreshing to see entries published almost daily. The consistent updates on engineering principles and systems notes make it easy to follow his ongoing work. Having new content most days means there is always something fresh to read about his latest projects and technical insights.

The profile shows extensive experience in AI systems and AdTech. Building platforms at Galvor and leading teams at Bizense indicates strong technical leadership. The daily engineering notes suggest a practical, hands-on approach to problem-solving. Is there a specific service tier for consulting on LLM pipelines, or is it project-based? The information provided is thorough enough to gauge expertise levels effectively for potential collaborations.

Reading through Mohit Rawat’s detailed working log offers a clear window into his engineering philosophy and technical depth. With fifteen years of experience spanning AdTech and messaging systems, he demonstrates a robust understanding of complex infrastructure. His work at Galvor on ad-creative intelligence platforms, utilizing LLM tagging pipelines and multimodal embeddings, highlights a sophisticated approach to solving real-world problems. Additionally, his background includes building AI outbound-sales platforms and leading engineering teams across international markets like India and Nigeria during the Bizense era. The daily entries, which are often short and unpolished, provide an authentic look at engineering principles and systems notes. This transparency in his thinking process, from problem definition to end-to-end solutioning, suggests a highly competent professional who values clarity and practical application in his AI development projects.