Personalized Recommendations
Design and production of a privacy-preserving recommendation system that increased meaningful interactions and included monitoring for feedback loops and bias detection.
📁 Portfolio
Selected projects that shaped product decisions
Kotik AI partners with product teams to design and deliver AI features that demonstrate clear business value and remain maintainable over time. Our portfolio includes systems built for personalization, retrieval-augmented assistants, and creative pipelines where provenance and safety are priorities. Each showcased project features an explicit problem statement, the experimental approach used to validate signal, and the production practices applied to keep models reliable. We focus on measurable outcomes such as lift in engagement, reduction in manual work, or improvement in time-to-complete tasks. Throughout delivery we embed monitoring, human review mechanisms, and governance so stakeholders retain control. The case studies below show how practical experimentation and rigorous engineering produce durable product improvements while mitigating operational risk.
Case studies & outcomes
Each case study begins with a concise hypothesis: the business impact we expect if a specific model or flow performs well. We run focused experiments to measure that impact, using rigorous evaluation metrics and human assessment where needed. When an experiment demonstrates signal, we harden it for production with automated tests, cost-aware deployment strategies, and monitoring that tracks data drift, model performance, and user-facing metrics. We also embed human-in-the-loop controls for high-risk decisions and provide clear rollback plans. Our documentation includes technical decisions, ablation results, and a governance checklist for model updates. The projects in this portfolio include a recommendation engine that increased task completion rate by designing interpretable features and safe personalization boundaries; a retrieval-augmented assistant that reduced support resolution time while keeping escalation clarity; and a creative generation pipeline that produced rapid design variants with provenance logs and a human review queue. The summaries emphasize reproducible experiments, measurable outcomes, and the operational practices that kept each system reliable after launch.
Conversational Assistant
A retrieval-augmented assistant integrated into internal tools, reducing average task time with safe fallbacks and confidence routing to human operators.
Generative Design Pipeline
A creative AI workflow producing curated assets with provenance logs and human-in-the-loop review to ensure brand consistency and safety.
See more or start a conversation
If you’d like to review full case studies, metrics, and technical notes for these projects, we can share detailed deliverables after a brief discovery. Contact us to schedule a walkthrough and discuss how similar approaches might deliver impact for your product roadmap.