Local context is everything: the facts on the ground, in the context of your problem, all extracted from unstructured documents.

Auto-organize that massive folder of documents into searchable insights and features.

Ohmense Local Context Engine

Matt Taylor

Matt Taylor


Matt Taylor


Matt is a veteran of storing, transporting and analyzing data at scale, low latency and industry leading sophistication.
Prior to founding Ohmense, Matt was CTO of Kensho, the Cambridge based technology and machine learning start up for the finance and intelligence industries valued at $500 million. Matt joined Kensho as a Software Engineer before becoming CTO in December 2014.
Prior to joining Kensho, Matt was Director of Engineering at Tumblr, up until its acquisition by Yahoo; a founding partner of a CT based high frequency trading firm; and Assistant Director of Infrastructure at FactSet.

Matt loves patterns, and delights in finding them in math, art, chess, nature and history. He escapes into long runs with no destination (although he has occasionally aimed himself at marathon finish lines). He lives in Belmont with his awesome wife and two kids.

Matt founded Ohmense upon the belief that data transparency, accountability and knowledge are the foundations for lasting success and value.

Matt applies finance-industry caliber technology and expertise in machine learning, data analysis, scaling and security--with a particular soft spot for education, civic efficiency, political accountability and non-profits.
Democratizing AI
MIT EmTech Digital MIT EmTech Digital 2016
ML in Your Browser
Google GCP Next GCP Next 2016

Ohmense: Local Context Engine

The power of data in context.

Our engine transforms and organizes unstructured data into yet-untapped connections, relationships, extractable insights and searchable knowledge.

Built by the expertise that helped create best-in-class machine learning and analytics Kensho, large scale distributed systems at Tumblr, and low-latency systems and research platforms for financial services.