Compresr
LLM context compression for better accuracy
What they do
LLM applications struggle with long context windows that increase token costs and reduce accuracy when important information is lost or diluted.
Compresr offers an API that compresses LLM context while preserving key information as a drop-in component for agents and RAG pipelines.
Developers and AI teams building LLM agents and RAG applications at startups and enterprises.
Founders
CEO @ Compresr. Previously researched LLM context compression as an EPFL PhD (Switzerland). Former Microsoft and Philips Research.
CAIO @ compresr.ai. On a mission to make every token count | EPFL CS | prev UBS
Co-founder & COO @ Compresr (YC W26) | EPFL Data Science Masters | ex-Bell Labs
Cofounder and CTO @ Compresr. Previously worked in research at EPFL’s DLab and AXA, focusing on efficient ML systems and prompt compression.
Company timeline
- Founded2026
- YC batchWinter 2026
- StatusActive
- Team size4 people
