SEO & AI Initiative
Brainly — Product Manager
Faced with Google’s SGE update threatening organic traffic, Brainly needed a scalable strategy to defend SEO rankings and maintain traffic acquisition.
Brainly’s organic search traffic was at serious risk due to Google’s new AI search features surfacing direct answers instead of traditional search listings.
The goal was to generate SEO-optimized topical content at scale to defend and grow Brainly’s visibility while minimizing development and operational costs.
- Led the exploration of scalable SEO content generation strategies using AI and scraping tools.
- Designed a Python-based system to orchestrate topical content creation across up to 1 million subjects.
- Built AI prompting frameworks for content generation aligned with educational standards and SEO best practices.
- Negotiated and aligned cross-functional teams (AI, Content, Product, Engineering) around a lean MVP plan, cutting proposed costs drastically.
- Supervised the web scraping and QA process to ensure topical accuracy and coverage.
- Reduced projected project costs from $300K to $15K by designing in-house MVP approach.
- Achieved 93% topical coverage and 70% QA accuracy across generated content.
- Delivered full MVP framework and initial data generation plan ahead of stakeholder deadlines.
Strategic Vision for a Personalized, AI-Driven Learning Experience
Brainly — Marketing Strategist
Brainly needed to evolve from a high-traffic homework Q&A platform into a more engaging, indispensable learning companion.
This self-initiated vision work aimed to define what that transformation could look like through a structured, personalized, and AI-augmented study experience.
Despite its scale, Brainly’s user experience was transactional — users came for one-off answers rather than long-term study support.
The opportunity was to design a product direction that shifted from isolated queries to structured, session-based learning journeys, aligning with user behavior, retention, and educational value.
- Conducted research to map user pain points and emotional friction in the current experience.
- Analyzed competitor platforms and user motivations to uncover opportunities for differentiation.
- Designed a modular product framework centered on guided “study sessions” combining AI-generated support, user goals, and progress tracking.
- Proposed features such as dynamic content personalization, dual-model answer comparison, citation generation, paraphrasing tools, and learning analytics dashboards.
- Integrated AI into user workflows not as a gimmick, but as a tool for confidence-building, clarity, and reduced cognitive load.
- Created a phased rollout roadmap, including MVP scoping, adoption strategies, and UX principles for long-term engagement.
- Delivered a complete strategic blueprint for platform evolution, presented to internal stakeholders.
- Elevated internal discussion around long-term product direction, AI integration, and session-based learning value.
- Positioned the platform to explore deeper engagement models beyond reactive Q&A loops.
Exploration of a Dynamic Metering System for Monetization Optimization
Brainly — Product Manager
As Brainly looked to evolve its freemium model, static access limits (e.g., fixed paywalls after X answers) created blunt user experiences and left revenue on the table.
There was a need to explore more nuanced, behavior-based metering to balance engagement and monetization.
The existing access model treated all users the same, regardless of behavior or intent — either offering too much for free or pushing away high-value users too early.
The opportunity was to design a dynamic metering system that adjusted content access thresholds based on user behavior, predicted conversion potential, and platform value delivery — increasing monetization without harming engagement.
- Conducted exploratory research on metering strategies used by subscription-based content platforms (media, edtech, productivity).
- Designed a scoring algorithm using behavioral inputs (session depth, feature usage, engagement history) to dynamically assign access thresholds.
- Proposed content gating flows (e.g., adwall, soft paywall, reward-unlock gates) tailored to user clusters.
- Used principal component analysis (PCA) and engagement signals to simulate user segmentation for prototype testing.
- Outlined experimental design for phased A/B testing, including fallback conditions and sensitivity analysis for impact on churn, engagement, and LTV.
- Delivered a full spec doc including architecture, scoring logic, threshold calibration strategy, and success metrics.
- Provided a technically and behaviorally grounded framework for adaptive monetization experimentation.
- Shifted internal thinking away from fixed thresholds toward intent-driven gating models.
- Enabled future pilots of metering strategies aligned with personalization and AI scoring tools.
- Positioned Brainly to scale monetization intelligently without compromising trust or learning outcomes.