Project details
- CTO / Product Lead
- AI & Generative AI
- jan 10, 2026
Lurqa – AI Companion App
Multimodal AI Companion with Emotional Memory Architecture
Lurqa is a 3D AI companion platform designed for sustained emotional engagement. The system combines voice interaction, long-term emotional memory, adaptive tone modulation, and visual embodiment to create a consistent and evolving AI presence. The objective was to move beyond transactional chatbot experiences and build an AI companion capable of personality continuity and contextual awareness across sessions.
Problem & Opportunity
- Most AI companions frequently reset conversational context
- Emotional responses often feel scripted or shallow
- User engagement declines without sustained personality continuity
- This created an opportunity to architect an AI system that preserves memory, adapts tone, and maintains character integrity over time.
Solution & Execution
- Designed long-term emotional memory architecture to preserve interaction history
- Built dynamic tone modulation aligned with user intent (romantic, friendly, mentorship modes)
- Developed a 3D avatar layer to enhance presence and visual immersion
- Tuned LLM prompt frameworks for personality stability and narrative consistency
- Structured multimodal interaction flows integrating voice, memory, and visual feedback
Impact & Outcome
- Increased emotional attachment and user immersion
- Extended session duration and repeat engagement
- Delivered a personalized AI experience with sustained contextual awareness
- Established scalable foundation for long-term companion AI systems
What This Demonstrates
- Long-term memory architecture for conversational AI
- Multimodal AI system design (voice + avatar + LLM orchestration)
- Personality-consistent prompt engineering frameworks
- Emotion-aware interaction modeling
- Human-centered AI product development at scale
Project details
- CTO / PM
- AI & Generative AI
- jan 10, 2026
Lurqa – AI Companion App
Project Overview
Lurqa is a 3D AI companion app built for long-term emotional engagement. The product combines voice interaction, emotional memory, and adaptive tone modulation. Users can interact in romantic, friendly, or mentorship modes. The focus was personality continuity, so the AI feels consistent over time. Every interaction builds on past conversations, creating a sense of presence and emotional awareness.
Problem & Opportunity
- Most AI companions reset context frequently
- Emotional responses feel scripted or shallow
- Users lose interest after short-term use
Solution & Execution
- Designed emotional memory architecture
- Built tone modulation based on user intent
- Developed a 3D avatar for visual presence
- Tuned LLM prompts for personality consistency
Impact & Outcome
- Stronger emotional attachment from users
- Longer session duration and repeat engagement
- AI experience that feels personal, not generic
Takeaways
- Memory drives emotional realism
- Consistent personality builds trust
- Multimodal AI increases immersion
Project details
- AI Product & Strategy Contributor
- AI & Generative AI
- jan 10, 2026
BCG X – Enterprise AI Systems
Project Overview
At BCG X, the focus was building enterprise-grade AI systems for retail clients. The work covered pricing optimization, markdown strategies, and promotion planning. These systems used large datasets and decision intelligence models. Reliability and scale were top priorities. Solutions had to integrate with existing enterprise infrastructure.
Problem & Opportunity
- Retail pricing decisions rely on outdated rules
- Manual promotion planning limits agility
- Enterprises need AI that works at scale
Solution & Execution
- Designed AI pricing and markdown models
- Supported promotion optimization workflows
- Ensured integration with enterprise systems
- Focused on robustness and reliability
Impact & Outcome
- Smarter pricing decisions
- Improved promotion effectiveness
- AI adoption within large retail environments
Takeaways
- Enterprise AI needs stability over novelty
- Integration matters more than features
- Decision intelligence drives real value
Project details
- Product Strategy / MVP Lead
- AI & Generative AI
- jan 10, 2026
Kwalifyr – AI Sales Copilot
Project Overview
Kwalifyr is an AI-powered sales copilot built for founder-led startups. The product helps teams qualify leads, prepare pitches, and improve sales conversations. The focus was speed, clarity, and decision intelligence. The MVP positioned AI as an assistant, not a replacement. Founders could move faster without adding sales headcount.
Problem & Opportunity
- Early-stage founders juggle sales with product work
- Inconsistent lead qualification wastes time
- Sales insights are often scattered
Solution & Execution
- Designed AI-driven lead qualification flows
- Built pitch guidance using structured prompts
- Created clear product positioning for investors
- Developed a founder-friendly pitch deck
Impact & Outcome
- Faster sales readiness for early teams
- Clearer messaging during investor conversations
- Strong MVP foundation for future scaling
Takeaways
- AI works best as a copilot
- Clarity beats complex automation
- Good positioning accelerates adoption
Project details
- Product & Documentation Lead
- AI & Generative AI
- jan 10, 2026
Overjet – AI Dental Insurance Platform
Project Overview
Overjet is an AI-driven dental insurance platform focused on underwriting and claims decisioning. The system supports documentation review and clinical workflows. The goal was accuracy, compliance, and explainability. AI models assist reviewers instead of making opaque decisions. The platform balances automation with regulatory trust.
Problem & Opportunity
- Manual dental claims are slow and inconsistent
- Underwriting decisions lack transparency
- Regulators require explainable systems
Solution & Execution
- Designed AI-supported underwriting workflows
- Built structured documentation standards
- Focused on explainable decision outputs
- Aligned product with compliance needs
Impact & Outcome
- Faster claim reviews
- Improved decision consistency
- Recognition as a LinkedIn Top Startup
Takeaways
- Explainability is critical in regulated AI
- AI should assist, not replace experts
- Documentation drives trust