Replacing Guesswork with Empirical Creator Intelligence
BehaveIQ was founded with a singular focus: to give YouTube creators and agencies the same statistical rigor, velocity analytics, and packaging intelligence that top media companies rely on.
Why We Built BehaveIQ
For years, YouTube creators have been advised to "make great content" and "upload consistently." While well-intentioned, this advice ignores the empirical mechanics of how the modern YouTube recommendation engine functions.
The algorithm does not measure effort; it measures cohort satisfaction, click-through probability against peer videos in browse feeds, and initial view velocity. When a creator spends 40 hours producing a video that underperforms, it is rarely a failure of editing—it is almost always a mismatch in conceptual packaging or topic velocity.
We built BehaveIQ to provide creators with transparent mathematical tools: isolating breakout outlier formats, benchmarking peer velocity, diagnosing packaging friction before publication, and extracting direct questions from audience comments.
Our Core Principles
Empirical Math Over Hype
We don't use proprietary "magic scores." Our outlier radar is based on transparent rolling median baselines and standard deviation calculations.
API Policy Compliant
We strictly adhere to Google's YouTube Data API v3 Terms of Service and Developer Policies. We never simulate bot traffic, scrape private data, or store unauthorized account credentials.
Engineering Excellence
Engineered with Next.js 15, FastAPI, and robust distributed caching to deliver fast, reliable analytics and real-time query responses.
The Engineering Behind BehaveIQ
BehaveIQ is architected and built by engineers focused on quantitative content analysis, recommendation system mechanics, and high-performance SaaS infrastructure.
Tarun Sharma
Founder & Lead Systems EngineerSystem Architect & Research Lead
Tarun designs the mathematical algorithms and distributed backend pipelines powering BehaveIQ. His work focuses on isolating signal from noise in YouTube recommendation feeds: deriving rolling-median view velocity multipliers, engineering multimodal computer vision models for thumbnail packaging, and building automated comment sentiment extractors.
Corporate & Operational Information
BehaveIQ Official Press & Media Kit
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