PORTFOLIO
This is a small sample of our portfolio companies.  When we are able to release names, due to confidentiality, the webiste will be updated accordingly.
 


 

Nesh is a AI Assistant for Search and Analytics designed to find answers to your business questions 10x faster via the power of Natural Language Processing. She combines the convenience of a Chatbot with the analytical and visualization capability of a business intelligence tool and the data search power of a cognitive search engine. She can connect to publicly available data or private data and allows users to have a natural conversation with it. 




 

Senslytics, headquartered in Atlanta, GA is an advanced AI software startup carving a new space "Lead Time Forewarning", giving managers and engineers more reaction time to optimize oil operations or curb emergencies.  We use Scientific Learning in our patented Intuition Technology framework and need fewer data for Forewarning.  We are commericializing the Contamination Forewarning application for Wireline Formation Testing Operation with Shell after delivering a successful one year long pilot trial with Shell GameChanger and are now expanding to Flow Assurance Forewarning use case. 


Equipcast is helping oil and gas operators produce more hydrocarbons with less. We are a cloud based SAAS Upstream Intelligence company. We are helping the industry solve the two biggest challenges. 1. Oil & Gas is fighting a generation gap & skills shortage. 2. Big Oil & Gas, tough it out or a business model reboot?

The solution is an economical and easy to use holistic web tool. That dynamically automates and manages their oilfields in real-time by creating a subject matter expert system to. 1. Save money. 2. Increase margins 3. Share knowledge.

From the Surface Systems to the Reservoir: Like Google “Search”. Our product “PageRanks” every single component that is instrumented in the field. It learns it’s behavior. Then provides the ‘search’ results and insights to take action!

Our value proposition is:
1. Integrate data and minimize lost production by prioritization of high performing wells
and system health.
2. Develop interoperable dynamic oilfield models. Extend run-life of equipment, manage the wells, reservoir, & field.
3. Aggregated Risk. Prognostic templates. Implement self-learning operational models in the field.