Tickr AI Research & Engineering

Research By

Ken Koski

Ken is a senior machine learning engineer at Tickr, leading on scalable machine learning workflows, MLOps, generative AI and deep learning. Ken has also worked as a data engineer with expertise in blockchain development, ETL optimization, and data infrastructure. He has made significant contributions to Canonical, Bitwise, and Ad Hoc. His skills extend to infrastructure development for Kubernetes and OpenStack, and full stack development.

Surpassing Frontier AI for CPG & Retail: Tickr AI beats OpenAI & Anthropic at Dynamic Hierarchical Product Categorization
Tickr's Dynamic Hierarchical Product Categorization offers CPGs and retailers a scalable, AI-powered solution that automates the task of categorizing products across evolving categories, significantly reducing manual effort and operational costs. Utilizing Tickr-LLM-base and Tickr-LLM-fine-tuned, the system achieves up to 98.1% accuracy, outperforming leading models like GPT-4o and Claude Sonnet 3.5. This superior performance enables businesses to streamline processes, enhance efficiency, improve downstream data science applications and reducing time spent on maintenance.
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Transaction Data with TimescaleDB
Time series data forms a critical component of business analytics, and in our pursuit of efficiency, we chose to evaluate TimescaleDB.
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Tickr AI Research & Engineering

Research By

Ken Koski

Surpassing Frontier AI for CPG & Retail: Tickr AI beats OpenAI & Anthropic at Dynamic Hierarchical Product Categorization

Transaction Data with TimescaleDB