Realizing the full potential of process automation will from now on rely on systems that generate their own code.  Systems architecture, data logic, visualization and analytics are the domain of sentient beings, the infrastructure should be managed automatically if we are to achieve any reliability at scale.

William Englis



Financial and legal advisor,  compliance, risk.


M&A and financings expertise.


Portfolio manager at Caxton  Risk arb at Wertheim

Investment banking at Dillon Read,  attorney at Davis Polk.

Wharton MBA (finance), University  of Pennsylvania


JD, Williams College  BA (economics).


Anthony Tomasic

VP Product


Technologist focused on applied machine learning, databases and IoT


Co founded Master of Computational Data Science (MCDS) and Master of Science in Product Management


(MSPM) at Carnegie Mellon



CoFounder of eXML Media

CTO Common Object

PhD, Princeton MBA, CMU

Faculty Stanford  University

Daniela Florescu



Senior Systems Architect:

Oracle, BEA, IBM, AT&T


Founder & CTO,


Significant Data and Query language Landmarks:

Integrated Information Processing Platform (IIPP)

Zorba VM,


and XQuery

Valerie Issarny

VP Research


Senior Research Scientist Specializing in middleware solutions, distributed and high performance computing, collaborative services, mobile services and interactions with sensors and actuators.


Senior Scientist Inria

CoFounder Ambenetic

Research Scholar UC Berkley

CoFounder Ambicity

(environmental data)


David Shantz



CMO multiple funded and acquired technology ventures


Epic Fusion MES (Epic Data)

Kalera (IoT Vertical Farming)

Quona Capital (Accion)

CoAlign (exit-Stryker)

Morphic (acquired)

HP Domain, SRI x 3

MPower (Morningstar)

Velosant (First Data)

The Bohan Group (analytics)

Navio DRM,

Actona (Cisco)

Altamira (Microsoft)

Virtualization: The Digital Twin

Digital twins were a huge step forward in maintaining industrial process flow and uptime.  Arica is in essence a virtual twin for your Industrial IoT solution.  It is now possible to identify the weak spots, the places where transport costs are overrunning budgets and possibly where performance anomalies from the wrong sensor choice are causing bad data before they start to make costly errors.


We can now spot problems well ahead of anything that even resembles a curve. The whole system gets out of the reactionary phase of fixing emergencies and problems to deliver constructive maintenance.


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