BehaveIQ Brand Assets, Factsheet & Executive Bios
Verified company facts, executive backgrounds, downloadable brand assets, and reusable copy for journalists, creators, analysts, and conference organizers.
Company Factsheet
Official Company Descriptions
Verified boilerplate copy for event listings, editorial coverage, directories, and podcasts.
"Data-Driven YouTube Creator & Growth Intelligence"
BehaveIQ is an AI-powered YouTube creator intelligence platform that replaces creative guesswork with empirical data. Using rolling-median baseline velocity math, multimodal AI thumbnail vision scoring, competitor benchmarking, and comment sentiment mining, BehaveIQ helps creators and agencies identify breakout outlier topics before they saturate.
BehaveIQ is a next-generation YouTube creator growth platform engineered to help video creators and agencies grow through empirical mathematics. Unlike legacy tools reliant on outdated search tags, BehaveIQ focuses on Browse and Suggested recommendation drivers: calculating view velocity deviations to detect breakout outlier videos, running multimodal computer vision checks on video thumbnails to predict click-through performance at mobile scale, and clustering thousands of viewer comments to uncover validated audience questions and friction points. BehaveIQ fully adheres to YouTube Data API v3 developer policies and statutory privacy standards.
Founded by systems engineer Tarun Sharma, BehaveIQ Creator Intelligence Technologies was built to bring quantitative engineering standards to YouTube content strategy. Platform recommendation disclosures confirm algorithmic feeds drive the substantial majority (estimated at 70% to 80% across high-growth channels) of YouTube watch time rather than manual search queries, yet creator tools have remained stuck in legacy search engine optimization models.
BehaveIQ solves this disconnection through four core algorithmic capabilities: (1) Outlier Detection, which evaluates view velocity against a rolling 90-day median to highlight high-performing format hooks irrespective of channel subscriber count; (2) AI Thumbnail Vision Analysis, which simulates mobile feed viewports at 320px to audit readability, contrast, and facial expression cues; (3) Competitor Benchmarking, providing real-time multi-channel velocity tracking across active rival catalogs; and (4) Audience Comment Mining, which parses public feedback into actionable content questions and viewer objections.
BehaveIQ operates with strict policy fidelity: all analytics are retrieved through official YouTube Data API v3 endpoints, without credential scraping or bot simulations, and payment infrastructure is secured via live Razorpay integration.
Executive Biography
Tarun Sharma
Founder & Lead Systems EngineerSystem Architect & Research Lead
Tarun Sharma is the founder and lead systems engineer of BehaveIQ. He designs the mathematical models, statistical pipelines, and computer vision systems powering the SaaS platform. Prior to BehaveIQ, Tarun researched algorithmic distribution patterns on YouTube, focusing on how cohort click-through rate, rolling baseline velocities, and visual packaging hierarchy impact recommendation engine discovery.
Original Creator Economy Research & Tools
BehaveIQ provides journalists, analysts, and creator researchers with reproducible public data assets, open mathematical specifications, and unauthenticated diagnostic tools.
YouTube Outlier Benchmark 2026
Ongoing empirical study measuring the statistical distribution of outlier multipliers across 5,000 public YouTube channels and 10 verticals.
Transparent Methodology
Open documentation detailing views-per-day velocity formulas, TF-IDF comment clustering, 320px mobile viewport simulation, and known limitations.
View Velocity Calculator
Free, public, unauthenticated diagnostic calculator that computes normalized views/day and classifies breakout outlier multipliers in real time.
Platform Interface & Feature Screenshots
High-resolution representations of BehaveIQ's analytical modules for editorial usage.
Isolates top-performing video hooks across 90-day catalog windows, decoupling algorithmic momentum from vanity subscriber metrics.
Audits thumbnail readability at smartphone viewport constraints, ensuring visual clarity before the video is exposed to browse feeds.
Official Brand Assets & Guidelines
When featuring BehaveIQ in articles, podcasts, or directories, please use official assets without alteration.
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Media & Interview Inquiries
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