Abstract: An artificial document generation method generating a larger set of artificial documents which mimics a smaller set of source documents. The method may include deriving metadata parameters from source documents, each parameter characterizing the source documents and having more than one possible value. The deriving may include: determining which ratio of the source documents has each of the values, defining metadata ratios characteristic of the source documents; training a model, on the source documents, to output artificial documents; and running the model as trained thereby to output artificial documents and overriding at least some words generated by the model in the draft artificial documents. Overriding is configured to ensure that at least some of the ratios characteristic of the smaller set of source documents are retained in the larger set.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: A system comprising a platform configured for communicating with enterprise end-users and for allowing the enterprise end-users to perform proof-of-concept testing for startups which provide respective enterprises with software products to be evaluated by the respective enterprises, the platform including processor functionality configured to analyze available information on enterprise data and, accordingly, generate metadata characterizing the enterprise data; generate artificial enterprise data conforming to the metadata; analyze available information on enterprise APIs and, accordingly, generate metadata characterizing the enterprise APIs; and generate at least one artificial API conforming to that metadata.

Abstract: A method for running proof-of-concepts for software solutions, including receiving, from an enterprise, an indication of network locations of servers in a production environment for a software solution selected from among plural candidates software solutions participating in a proof-of-concept running in a proof-of-concept (aka PoC) environment on a PoC platform; providing at least one recording, uploaded onto the platform, of traffic between the servers in the production environment; providing a mapping of the network locations to, respectively, PoC platform local network addresses of servers within the PoC environment; adapting the recording by replacing each occurrence of an individual one of the network locations, within the recording, with a PoC environment server local network PoC platform address to which the individual one was mapped, thereby to generate at least one adapted file; and replaying the at least one adapted file on the servers within the PoC environment.

Abstract: A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.

Abstract: A computerized computer software pilot evaluation method for quantifying performance of a first population of start-up end-users, each start-up end-user contending within at least one software pilot defined by, including performing a software task for, an enterprise end-user from among a second population of enterprise end-users, the method comprising: generating at least one neural network model, using a processor, for at least one startup server participating in at least one enterprise's pilot (aka computer software pilot), the computer software pilot comprising a task to be performed by each of at least one startup server participating in the pilot, and using the at least one neural network model to predict at least one derivable kpi from measurable kpi's generated by the startup server within the enterprise's pilot.

Abstract: An end-to-end “pilot as service” system for software pilot testing, comprising a server including at least one processor and operative for generating a set of software testing environment/s simultaneously serving plural clients from among a first plurality of enterprise clients, the environment being operative for concurrently running plural proof-of-concept tests for respective plural software products each being tested (“pilots”), on behalf of an individual start-up client from among a second plurality of start-up clients, by an individual one of the population of enterprise clients; a registration functionality, on the processor, operative for pre-registering at least one of: enterprise clients from among the first plurality of enterprise clients, and start-up clients from among the second plurality of start-up clients; and a dashboard functionality, on the processor, operative for presenting at least one result of at least one proof-of-concept test to each client which has pre-registered using said regist

Abstract: A system comprising a platform configured for communicating with enterprise end-users and for allowing the enterprise end-users to perform proof-of-concept testing for startups which provide respective enterprises with software products to be evaluated by the respective enterprises, the platform including processor functionality configured to analyze available information on enterprise data and, accordingly, generate metadata characterizing the enterprise data; generate artificial enterprise data conforming to the metadata; analyze available information on enterprise APIs and, accordingly, generate metadata characterizing the enterprise APIs; and generate at least one artificial API conforming to that metadata.

Abstract: An end-to-end “pilot as service” system for software pilot testing, comprising a server including at least one processor and operative for generating a set of software testing environment/s simultaneously serving plural clients from among a first plurality of enterprise clients, the environment being operative for concurrently running plural proof-of-concept tests for respective plural software products each being tested (“pilots”), on behalf of an individual start-up client from among a second plurality of start-up clients, by an individual one of the population of enterprise clients; a registration functionality, on the processor, operative for pre-registering at least one of: enterprise clients from among the first plurality of enterprise clients, and start-up clients from among the second plurality of start-up clients; and a dashboard functionality, on the processor, operative for presenting at least one result of at least one proof-of-concept test to each client which has pre-registered using said regist