Chhalak Pathak

Chhalak Pathak

Chhalak Pathak has experience in content marketing, SEO, AI-powered marketing and B2B communications, with a passion for turning complex ideas into content that people can actually understand and engage with. Her work spans blogs, case studies, thought leadership, social media and marketing campaigns across technology, fintech and emerging digital trends.

Recent Posts

AI in OTT: Personalization, Content Discovery and Ad Optimization

Key Takeaways: AI in OTT helps streaming platforms personalize recommendations, improve content discovery, and optimize advertising. AI-powered personalization uses viewing history and preferences to improve engagement and subscriber retention. AI-driven content discovery supports semantic search, multilingual recommendations, and automated video tagging. Artificial Intelligence in OTT advertising enables audience segmentation, targeted ads, dynamic ad insertion, … Continue reading "AI in OTT:…

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OTT App Development Cost: Features, Platforms & Timeline

The growing demand for online video streaming has created opportunities for businesses to launch their own OTT platforms for entertainment, education, sports, and live events.

Topics: OTT Apps
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RAG in Healthcare: Use Cases, Compliance & ROI

What Is RAG in Healthcare? Retrieval-augmented generation, or RAG in healthcare, is an approach that allows a generative AI system to find relevant information from trusted healthcare sources before creating a response.

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Software Architecture for AI Systems: Patterns & Components

Software architecture for AI systems defines how models, data, applications, APIs, business tools, security controls, monitoring, and infrastructure work together as one system.

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FinOps for AI: Managing Token Spend Like Cloud Infrastructure

AI can make a feature more useful, but it can also make its costs harder to predict. Unlike a fixed software license, AI spending can change with usage, the number of model calls, the amount of information processed, and the models involved.

Topics: CloudOps DevOps
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Building Token-Efficient Architecture for AI Applications

A product team can spend weeks building an AI feature that works perfectly during testing. The demo looks good, the team is happy with the results and the feature is ready for real users. The problem often appears later, when thousands of people start using it and the AI bill begins to grow much faster than expected.

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