Foundational AI Research Lab for Efficient Language Modeling

ZettaQuant is building an AI infrastructure layer for the AI stack, focused on a simple but increasingly expensive problem: the unit economics of tokens.

Our Kolmo model family packs relevance filtering into a sub-100M parameter footprint. Instead of changing the downstream workflow, ZQ removes irrelevant context before it reaches the language model, reducing inference cost while preserving answer quality, traceability, and existing agentic workflows.

We judge solutions on more than accuracy alone: cost, latency, replicable decisions for compliance, and security. Relevance is a context problem, not a reasoning problem, and it should not run at frontier prices.