summary
Entrepreneur turned AI researcher specializing in Large Language Model behavior patterns and Generative Engine Optimization (GEO).• Currently I am mapping how Large Language Models think, behave, and discover information.
• Investing in the future of human-AI interaction.Previously (2014-2024)• Founder of Inclov, the world's first matchmaking platform for people with disabilities.
• Founding Member & Product Strategy Lead, Jupiter Money
• Product Strategy Consultant, The Yoga Institute, Nispand Wellness App
entrepreneurship (2014-2023)
Co-founded Inclov, the world's first matchmaking app for people with disabilities
Served 100,000+ users across India
Won UN Top 50 Global Innovative Practices, featured in Forbes, BBC, The Guardian
I was also a founding member of Jupiter , the neo-banking startup and led the product strategy and growth for the company in its early days.
Due to my work at Inclov, I received a special invitation to meet with Hon'ble Prime Minister Shri Narendra Modi and his cabinet including the late Shri. Arun Jaitely and late Shri. Sushma Swaraj. This was an important moment because I was able to provide value to the society in deeply profound ways which has been rare and unusual for me.
research and investments (2025)
Studying how Large Language Models discover and prioritize content check my research notes
Developing non-technical frameworks for understanding AI behavior
Pioneer in Generative Engine: Optimization (GEO) and Answer Engine Optimization (AEO)
Publishing research on GitHub and The Third Frontier newsletter
investments
Accacia: decarbonising real estate with climate-grade products and carbon intelligence. read B capital note on the investment
Exponent Energy: 15-minute rapid charge for EVs (funded by Hero Group post my investment)
Metastable Materials: lithium-ion battery recycling (entered sequoia surge program, at 8x multiple)
Biddano: healthcare supply chain platform that provides a simplified order management technology for chemists and hospitals.
Llama: exited in 2025
Styleuai: image gen-ai to create model-ready product visuals, boost SEO and launch new inventory faster.
Neusearch.ai: Get your brand discovered by AI.
areas of investment/interest
Generative AI Applications: Image, text, and video generation tools. check my geo-research
AI Infrastructure: Tools for AI visibility, monitoring, and optimization [AI enbablers]
Model Behavior Solutions: Platforms addressing the interpretability gap. check my geo-research
AI-Native Interfaces: New ways humans interact with AI systems
Investment Range: $5,000 - $10,000 per venture
what I bring to the table
Deep understanding of LLM behavior patterns. Deep dive here
Practical insights from weekly model behavior experimentation and AI research
Product strategy, UI/UX expertise (+ extended team, hiring support if needed)
writing @ The Third Frontier (AI's impact on human experience)
Through my writing on Substack, I examine AI companions and human loneliness, search algorithms and collective memory, interface design and behaviour change. Everything is always from a place of genuine curiosity about what's actually happening to us. Nothing performative, nobody worth impressing.
love in the times of liquid neural networks: Liquid AI, spun out of MIT, has built something called Liquid Foundation Models using liquid neural networks. This note is my philosophical take on what this means for humans.
Move 37: AlphaGo, my next move and why AI research is so important right now
The mystery of artificial minds: On intuition, consciousness and the billion-parameter mystery we can't cracky
What octopuses, bees, and AI teach us about awareness? primatology question
my favourite AI writers on the internet
Applied Large Language Model Behavior Research and Generative Engine Optimization
Research OverviewApplied behavioral research studying how Large Language Models behave, interpret content, and make decisions.Through systematic experimentation and empirical analysis, this research explores the interpretability gap between what can be measured and what can be understood about LLM behavior.
Core Research1. How do different base models handle brand mentions differently across ChatGPT, Claude, Gemini, and Perplexity.
2. What biases exist in pre-trained models and how do they affect content discovery.
3. Why do identical queries produce different responses across models and within the same model.
4. How can brands optimize for AI discovery when AI behavior itself remains unpredictable.Full information on my Github• Last Research Update: Sept 3rd, 2025
• Next Deep Dive Research: Multi-modal AI behavior analysisRrelease: DOI: 10.5281/zenodo.17063064
media
books and podcasts
tv and keynotes
fellowships
AIYD Fellow • Visit
INK Fellow • Visit
Zero Project • Keynote Speaker and Award Winner
Rajeev Circle Fellow (MJF) • Visit
personal life
As an introvert with limited social life, I spend disproportionate time of my day exploring one of the many rabbit roles, meditating or reading books.
I love sushi and good coffee
Due to my 900+ hours yoga-meditation training from The Yoga Institute and 1000 hours of yoga therapy practice from Kaivalyadham Yoga Institute, I also teach yoga to a few selected students on the weekends
I write meditations; a personal newsletter that serves as a web-based diary exploring the depths of shared human experience.
Living between Bombay and Bangalore, India
contact
AI Research by Kalyani KhonaApplied Large Language Model Behavior Research and Generative Engine OptimizationResearch Overview
Applied behavioral research studying how Large Language Models behave, interpret content, and make decisions. Through systematic experimentation and empirical analysis, this research explores the interpretability gap between what can be measured and what can be understood about LLM behavior.Major Research FindingsThe Interpretability Gap in LLM Behavior
While platforms build sophisticated tracking systems for Answer Engine Optimization, the same query asked by different people can yield entirely different LLM responses. Research demonstrates we can measure what happened, but understanding why it happened remains elusive.ChatGPT Search Triggering Inconsistency
Discovery that ChatGPT web search triggering is fundamentally inconsistent. Identical queries in incognito sessions can yield completely different behaviors, sometimes triggering web search, sometimes relying purely on training data. This means perfect content optimization may still yield zero visibility due to factors outside brand control.Model-Specific Content Preferences
Each frontier lab uses different fine-tuning approaches, creating distinct content preferences. ChatGPT prefers structured lists, comparison tables, and actionable content. Claude favors reasoning chains, nuanced analysis, and academic sources. Gemini emphasizes authority signals, citations, and factual verification. Perplexity prioritizes real-time content and source diversity.Semantic Monopoly Problem
Certain brands achieved deep semantic association during model training periods. CRM queries default to HubSpot and Salesforce. Spreadsheet queries default to Excel. Video conferencing queries default to Zoom. New brands cannot compete head-on with these semantic monopolies and must find niche positioning strategies.Content Visibility Framework
Content must pass through six distinct filters to achieve AI visibility. Crawlability for technical accessibility.
Parsability for clear structure extraction.
Relevance for query-content alignment.
Efficiency for high semantic density processing.
Confidence through authority signals and factual consistency.
Synthesis Priority based on computational ROI for inclusion.Research MethodologyDaily Experimentation Protocol
Systematic testing of query variations across multiple LLM platforms with consistent documentation. Testing framework includes same queries across ChatGPT, Claude, Gemini, and Perplexity. Multiple time periods to identify temporal patterns. Incognito sessions to minimize personalization effects. Documentation of response variations and synthesis patterns.Behavioral Pattern Recognition
Focus on identifying observable behavioral constants and triggers across different models rather than attempting to reverse-engineer algorithms. Key metrics include appearance rate for frequency of brand mentions. Synthesis depth for how thoroughly content is processed. Citation position for placement within AI responses. Sentiment preservation for accuracy of message conveyance.Cross-Model Comparison Studies
Comparative analysis of how different models handle identical queries, with focus on response structure, citation patterns, and content preferences. Research includes model temperature variation studies, query routing optimization tests, and multi-conversational prompt analysis.Key ContributionsInterpretability Gap Theory
The gap between what can be measured and what can be understood in LLM behavior is not a problem to solve but the reality to navigate. Supporting evidence includes identical queries producing different responses, perfect content optimization yielding zero visibility, and unpredictable web search triggering patterns.Semantic Positioning Strategy Framework
Instead of competing with established semantic monopolies, new brands should focus on industry-specific positioning, use-case optimization, and problem-first discovery content that intercepts user problems rather than category searches.Multi-Modal Optimization Paradigm
As LLMs evolve toward multi-modal capabilities, content optimization must expand beyond text to include voice query optimization, visual content for AI parsing, and cross-modal semantic consistency.Applied Research ApplicationsCollaboration with Neusearch
Research findings are actively applied in portfolio companies Neusearch, providing real-world validation of theoretical frameworks.Implementation includes GEO optimization strategies, semantic positioning methodologies, and content structure optimization for AI discovery.Startup Optimization Strategies
Practical frameworks for optimizing startup content for AI discovery, including semantic positioning strategies, content clustering methodologies, and competitive positioning in AI responses.Research provides actionable frameworks for businesses including content optimization checklists and model-specific strategies.Investment Thesis Development
Research-backed approaches to evaluating AI startups, understanding LLM market dynamics, and predicting technology adoption patterns in the AI discovery landscape. Analysis includes how AI discovery affects customer acquisition channels and investment risk assessment.Current Research ProjectsMulti-Modal Search Optimization
Investigation into how AI systems handle text, image, and voice queries, with focus on optimization strategies for emerging multi-modal interfaces. Research includes voice commerce optimization and cross-platform behavior mapping.Citation Framework Analysis
Systematic study of what makes content quotable by AI systems, including analysis of authority signals, content structure, and semantic relationships. Focus on real-time signal integration and impact of fresh content on model responses.Query Intent Evolution
Research into how user behavior with AI systems evolves over time, including analysis of prompt engineering adoption and changing interaction patterns. Studies include user interaction pattern evolution and AI query sophistication tracking.Research Impact and ApplicationsIndustry Influence
Research methodologies are being adopted by startups and enterprises for AI optimization strategies. Frameworks are influencing how companies approach brand positioning in AI-driven discovery and go-to-market strategies for AI-native companies.Academic Contribution
Applied research approach provides accessible frameworks for understanding LLM behavior while maintaining methodological rigor and practical applicability. Research is published under Creative Commons Attribution for educational and research purposes.Business Strategy Applications
Research directly impacts product discovery strategies, competitive analysis through AI-driven brand monitoring, and market positioning navigation around semantic monopolies in AI responses.Future Research DirectionsImmediate Research Focus
Multi-modal content analysis examining how images, videos, and audio influence AI responses. Real-time signal integration studying impact of fresh content on model responses. Cross-platform behavior mapping with comparative analysis across emerging AI platforms.Long-term Research Goals
Training data influence studies examining how content becomes part of future model training. User interaction pattern evolution tracking changes in AI query sophistication. Regulatory impact analysis studying how AI transparency requirements affect brand strategy.Collaboration Opportunities
Academic partnerships working with universities on LLM interpretability research.Industry studies collaborating with brands on real-world GEO implementation. Platform integration through direct research partnerships with AI platform providers.Research Documentation and AccessOpen Research Methodology:All experimental methodologies are publicly documented with systematic testing protocols, measurement frameworks, and replicable analysis methods. Research includes daily experiment logs, comparative analysis data, and practical implementation frameworks.Current Research Status:Active and ongoing research updated regularly with new findings and methodologies. Research period spans 2024-2025 with daily experimentation logs and systematic behavioral pattern documentation.Research Repository Statistics
Research documents with daily experiments logged across six months of active research.
Documentation includes systematic testing protocols, cross-model comparison studies, and real-world portfolio company applications.---Research Contact: Available for collaboration and consultation on LLM behavior research and GEO strategy implementation. Connect via LinkedIn, The Third Frontier publication, or email [email protected] for research collaboration opportunities.