GENESYS Integrations

Graphlogic Conversational AI Platform consists of: Robotic Process Automation for Enterprises (RPA), Conversational AI, and Natural Language Understanding technology to create advanced chatbots and voicebots. It also includes Automatic Speech Recognition (ASR), Text-to-Speech solutions (TTS), and Retrieval Augmented Generation pipelines (RAGs) with Large Language Models. Key components: Conversational AI Platform - Natural Language understanding - Retrieval and augmented generation pipeline or RAG pipeline - Speech to Text Engine - Text-to-Speech Engine - Channels connectivity API Builder Visual Flow Builder Pro-active outreach conversations Conversational Analytics - Deploy anywhere (SaaS, Private Cloud, On-Premises). - Single-tenancy / multi-tenancy - Multiple language AI

Chatterbox, an open-source voice cloning AI model created by Resemble AI and distributed under the MIT license, allows users to perform zero-shot voice cloning with just a five-second sample of reference audio, thereby removing the requirement for extensive training. This innovative model provides expressive speech synthesis that features emotion control, enabling users to modify the expressiveness of the voice from a dull tone to a highly dramatic one using a single adjustable parameter. Additionally, Chatterbox allows for accent modulation and offers text-based control, which guarantees a high-quality and human-like text-to-speech output. With its faster-than-real-time inference capabilities, it is well-suited for applications requiring immediate responses, such as voice assistants and interactive media experiences. Designed with developers in mind, the model supports easy installation via pip and comes with thorough documentation. Furthermore, Chatterbox integrates built-in watermarking through Resemble AI’s PerTh (Perceptual Threshold) Watermarker, which discreetly embeds data to safeguard the authenticity of generated audio. This combination of features makes Chatterbox a powerful tool for creating versatile and realistic voice applications. The model's emphasis on user control and quality further enhances its appeal in various creative and professional fields.

A Digital Thread is essentially a graph consisting of nodes that represent various elements found in enterprise repositories, tools, and systems for version control, with edges denoting both intra-model relationships within each tool and inter-model connections that Syndeia facilitates between these nodes. Additionally, Syndeia offers model transformation capabilities that help construct the digital thread graph, allowing for operations such as seamlessly dragging and dropping requirements from Jama or DOORS-NG into SysML, generating Simulink models and PLM part structures derived from SysML models, linking behavior elements in SysML to corresponding software code in GitHub, and even monitoring the development progress of a sub-system in JIRA directly from SysML. Furthermore, Syndeia enhances the functionality of the digital thread by providing services for searching, comparing, and bi-directionally synchronizing interconnected models, enabling users to compare and synchronize alterations in system architecture with the PLM part structure or align changes made in DOORS-NG requirements with SysML, illustrating the comprehensive capabilities of digital thread management. Ultimately, this interconnected approach not only streamlines workflows but also ensures that all components of a project remain aligned and up-to-date across multiple platforms.

BigLever's exceptional proficiency in guiding organizations toward successful Product Line Engineering (PLE) has led to the development of a comprehensive and repeatable framework for organizational transformation known as onePLE™. The term onePLE signifies our commitment to delivering a singular, all-encompassing solution that encompasses everything necessary to establish, launch, and effectively manage your Feature-based PLE Factory, bolstered by a team of highly trained PLE executives, managers, technical leaders, and practitioners. This innovative approach is designed to cater to organizations of all sizes, whether it be a large enterprise, a division, a business unit, or even a single product line-focused organization. Furthermore, the Asset Supersets, depicted in the bottom left, serve as the shared digital assets for a product line, ensuring that there is no duplication of any asset, thereby maximizing efficiency. In essence, onePLE stands as a testament to our dedication to fostering sustainable PLE practices that drive long-term success.

A Feature-based Product Line Engineering (PLE) Factory functions similarly to a traditional manufacturing facility, but it focuses on digital assets rather than tangible components. To build this factory, your organization develops a comprehensive “superset” supply chain of digital assets that are accessible across the entire range of products. These assets come with all the available feature options provided in the product lineup. The selected features for each product are detailed in the Bill-of-Features, and subsequently, a product asset instance is generated using the Gears product configurator. The PLE Factory, driven by Gears, transforms into an automated production system that assembles and configures the shared digital assets according to the chosen features for each product variant at the simple push of a button. With BigLever’s Gears, your organization benefits from a unified set of PLE concepts and frameworks, enhancing your tools and assets, which ultimately streamlines engineering processes throughout the entire product lifecycle. This integration not only promotes efficiency but also fosters innovation within product development.