Optimizing Search & Data Processing with Self-hosted SLMs

Daniel Svonava and his team at Superlinked have been focused on AI-powered search and data processing systems since 2021, running 1TB+ vector search indexes in e-commerce and enterprise search. In 2026 they are gearing up for a launch of their most ambitious project yet - a fully open source inference engine & cluster software, aiming to democratize the access to less well known but sorely needed models from multi-vector vision capable search models to relationship-extracting SLMs.

Trey is lead author of the book AI-Powered Search and is the founder of Searchkernel, a software company building the next generation of AI-powered search. He is an advisor to several startups and adjunct professor of computer science at Furman University. He previously served as CTO of Presearch, a decentralized web search engine, and as chief algorithms officer and SVP of engineering at Lucidworks, an search company whose search technology powers hundreds of the world’s leading organizations. Trey in an instructor for the AI-Powered Search course on Maven.

In 2012, Doug got bit by the search bug and he's still trying to keep up. From full-text search, to Learning to Rank models, to search agents that generate their own code, he knows the endless landscape first hand. Yet Doug wants to deeply understand the what / how / why. He wants to help teams use these technologies practically, distinguishing hype from reality.

He’s led search at Reddit, Shopify, and Wikipedia, authored Relevant Search and AI Powered Search, and advised 100+ organizations over the years - all in pursuit of the same question: how does search actually work?