Karol Kulasiński

I designed and co-implemented a platform for creating, customizing, and applying AI agents. The agents provide an additional layer of security, thanks to the anonymization service that protects 100% of the data coming out of the client's server. The agents can learn from previous tasks and can be spawned via chat, API, or Jira.

I created subMIND, a tool for extracting subconscious information from a large body of text. From relations between characters in a book to suspicious activities in your report files, subMIND uses state-of-the-art techniques for entity recognition, graph relations, and visualizations. This tool is intended for researchers of all kinds.

An automated system for scraping, analyzing, and detecting hot topics from online articles.

I predicted the topics using the LDA method and ran collected texts through BERT that could, at the end of the day, determine what target audience does the specific text pertains to. Texts were scraped from LinkedIn and online newspapers.

I led a data science team of eight people that created the complete pipeline for a price optimization product for online ads.

The ads are first-price auctions, and we used ML techniques to find their optimum price and predict the price that would allow the end customer to maximize their revenue. Thanks to our ML models, the revenue boost was up to 10%.

I created an entire ML pipeline, including data ingestion, testing, prototyping, error handling, monitoring, and evaluation. I also directed the product development process from the R&D side, including hypothesis testing and handling client feedback.