Abseil Automation - Machine Learning and Optimization Consulting

About

Pavel Konov, MS

CEO

Pavel spent his early career at Palantir solving hard quantitative consulting problems in healthcare and finance, then built production ML and robotics systems at Strateos and Trace Genomics. Prior to Silicon Valley, Pavel worked as a Physicist at Fermilab and several research groups at UPenn.

Pavel holds an MS in Physics, and BA in Physics and Philosophy from UPenn.

On a per-project basis, Abseil has also been fortunate to work with exceptionally talented individuals, the majority of whom have gone on to found venture-backed startups that have collectively raised over $30M.

Clients

Download case study
Zymergen's production scheduling problem was too complex for off-the-shelf solvers or manual planning. Abseil built a custom solution that drove 1.3–3.4x throughput gains across ~$100M in capital assets while baselining correct manufacturing performance and drastically reducing future capex.

Abseil delivered one of Toyota's most successful AI initiatives globally from concept to prototype (off-line trial) to production line: an advanced Machine Learning-based vehicle welding controls system that produced direct cost savings of $XX M/yr in the US alone, in addition to substantial worker safety benefits.

Our Services

Machine Learning & AI

Production ML systems, from LLM-powered applications to custom deep learning for vision, Natural Language Processing (NLP), time-series analysis, and other use cases.

Optimization

Factory automation, production scheduling, and logistics routing using Constraint Programming and Mixed Integer Programming. These optimization techniques solve traditionally intractable operations challenges.

Software Engineering

Enterprise-grade code informed by experience at Palantir and leading Silicon Valley companies. Our engagements deliver tested, production-ready systems deployed on AWS, GCP, or your infrastructure of choice.

We build AI that works on the factory floor