Expanding GenAI Horizons: Through the Lens of EIQ’s Narrative

Expanding GenAI Horizons: Through the Lens of EIQ's Narrative

As the digital world is going through a hyper-frenzy over Generative AI (GenAI), it is natural that intelligent business automation (or as some call it, hyperautomation, end-to-end automation) also is undergoing massive amount of change. Making GenAI seamlessly work with new or existing applications and processes through the existing automation frameworks has become a major boost for automation providers.

EvoluteIQ has always been leading the overall enterprise automation and low-code/no-code platforms by bringing Data, Events, Processes, and ML together since its inception. The EIQ Platform is the only comprehensive, end-to-end intelligent business automation platform with a unified orchestration. It includes integrated RPA, Process, Data, Events, ML, and Blockchain as flows with business rules, connectors, and analytics support where enterprises can build simple to complex applications through visual flows as low code/no code studio to build web or mobile apps.

With the advancement of AI/ML into GenAI, the EIQ Platform had to find its foothold with its offering that included a seamless AI/ML extension along with GenAI offering.

Alignment of Generative AI with Intelligent Business Automation

Since early 2021-22, many leading IT analyst companies identified the technology trends that encapsulate a diverse array of advancements driving the evolution of the digital realm. These trends include data fabric, cybersecurity mesh, composable applications, distributed enterprise, GenAI, autonomic systems, hyperautomation and the like. Each trend represents an effective force reshaping industries and influencing how businesses operate.

However, potential problems arise because when technology trends are realigned and repositioned with an enterprise outlook, they might not effectively address the specific needs and challenges of the organization. In other words, the translation of trends into practical solutions for the enterprise environment might encounter discrepancies or overlook critical aspects essential for successful implementation and integration within the business framework.

  • There are disparate data sources within an enterprise – they may be available in enterprise applications such as ERP/CRM, within RDBMS, in big data stores, or in the form of unstructured data.   
  • This data needs to be brought under one roof through various pipelines. That is called Data Fabric. Data fabric is an architecture that facilitates the end-to-end integration of multiple data pipelines and cloud environments.  
  • Based on this data, enterprises need to build decision intelligence through traditional business rules management as well as bring in AI/ML.  
  • Intelligent Applications are built through AI Engineering to create AI systems following human needs for mission outcomes.   
  • As Enterprises are becoming Distributed (Distributed Enterprises), the need to support cloud-native platforms and privacy-enhancing computations is of primary importance. Every single user, role, and group has different access rights and is to be allowed through a comprehensive Cyber Security Mesh.  
  • All these constructs need a GenAI construct to keep learning, analyzing, and generating various types of content cutting across text, imagery, audio, and synthetic data that can be used to build intelligent applications. This ultimately leads to Autonomic Systems that help manage themselves automatically through adaptive technologies that further computing capabilities and cut down on the time computer professionals need to resolve system difficulties and other maintenance such as software updates.  
  • Only through these layers can an intelligent app give total experience through various channels.   
  • These systems can be built through traditional development techniques, which will take a long time to enable – while platforms like EIQ can make this happen through its intelligent business automation and composable applications platform.

In other words, the following image depicts how the EIQ Platform is aligned to the tech trends through its various offerings and features:

The EIQ Approach

The EIQ Platform framework boasts a diverse array of tools, ranging from RPA to processing, data, and event flows. This comprehensive framework encompasses transaction-oriented systems, exemplified by RPA for task automation and process flows for transaction automation. These functionalities facilitate various interactions, including person-to-person, person-to-system, and system-to-system.

In executing these interactions, a substantial amount of data is generated and categorized into master or transaction data. Depending on the volume, this data can be stored in either a Relational Database Management System (RDBMS) or a Big Data structure, and it may reside in the cloud or on-premises. This represents data at rest.

However, the event flow involves the continuous influx of data, constituting data in motion. Traditionally known as Complex Event Processing, this dynamic process is now referred to as Event Streaming engines or Event Stream Processing in modern parlance. In the contemporary landscape, these event stream processing engines and data flows demand intelligence to meet the expectations of modern enterprises.

As enterprises increasingly seek more intelligent systems, there is a growing anticipation for these systems to possess inherent knowledge of policies, processes, and rules. This encompasses a need for intelligence ingrained into the framework to handle and process data efficiently, making it an integral aspect of contemporary enterprise applications.

EIQ Intelligent Business Automation Platform

GenIQ's Connectivity Arsenal: Empowering Your Data Journey

The EIQ Platform introduces the innovative GenIQ feature, a robust offering encompassing the GenAI Framework. This feature incorporates custom-tuned out-of-the-box LLMS & Transformers, catering to diverse needs in Natural Language Processing (NLP), Image, Audio, and Multimodal applications. Moreover, it boasts a Bring-your-own-model (BYOM) capability, enabling enterprises to seamlessly integrate their own meticulously researched or fine-tuned LLMs/Transformers. The platform also facilitates integration with major ML/LLM Frameworks such as HuggingFace and NeMo.

In addition to its cutting-edge GenIQ feature, EvoluteIQ provides comprehensive support for traditional AI/ML through the EIQ ML Studio, including algorithms and Jupyter Hub. This versatile platform ensures a seamless fusion of state-of-the-art Generative AI with conventional AI/ML methodologies.

The integrated hyperautomation environment, powered by GenIQ, is a distinctive highlight of the platform. This environment inherently incorporates Process flows, Data (Batch), Event (Streaming), Rules & Constraints, Mobile, and RPA Automation. It represents a unified solution that empowers enterprises to harness the full spectrum of automation capabilities, seamlessly integrating generative AI with traditional AI/ML approaches within a cohesive and efficient framework.

GenIQ Features and Differentiators

The EIQ Platform is equipped with an extensive array of plugins and connectors, surpassing 250+ in number with over 3000 connectors connecting to diverse third-party systems, spanning from SAP ERP and Salesforce to CRM systems and other enterprise-class systems. In a groundbreaking move, EvoluteIQ has adopted a data-centric approach, transforming connectors into data pipes rather than conventional Enterprise Service Buses (ESB) or iPaaS, providing unparalleled flexibility and power to the platform.

This innovative approach, which treats connectors as pure data pipes, empowers the EIQ Platform’s data ingestion layer for data flow and real-time data scenarios. This means querying third-party systems is as seamless as querying a local database. Within generative AI, EvoluteIQ leverages these data pipes for data injection, supporting real-time and batch data. The injection service allows multiple events and data sources to be injected into a central data store.

In the ML Flow module of EvoluteIQ, data preparatory steps are undertaken in various user interfaces, such as financial, insurance, or healthcare domain-specific data preparations. The prepared data can then be used as a pipeline into ML Flow’s data segregation nodes, encompassing training and testing data and data evaluation. Evaluated data enters different model registries, including Regression Pipeline, Classification Pipeline, Association Pipeline, Neural Net Pipeline, and Transfer Learning.

The ML Flow model building nodes feature out-of-the-box LLMs and various transformers, from LLaMA to GPT to BERT, accommodating pre-trained models and allowing enterprises to integrate their own models using H2O.ai, spaCy, scikit-learn, TensorFlow, or Hugging Face models.  

The model registry can be extended through model inference using ML Flow model inference nodes, where various model handlers can be written as services. These models can be utilized in UI layers: data flows, event flows, process flows, RPA, and rules engines. The usage can be as batch processes, real-time inference, conversational inference, or REST APIs, showcasing the versatility of the EvoluteIQ Framework.

The GenIQ feature set within the EIQ Platform encompasses preprocessing, building, and actualization stages. During preprocessing, the platform aids in data collection and processing through various connectors, offering insights through transformations and various feature stores. In the build stage, the platform provides structures for pre-built algorithms, pluggable frameworks, custom algorithms, and model catalogs. The action section facilitates deployment and usage through deployment pipelines, application creation, and inference APIs via the traditional EvoluteIQ App Studio.

The overall model management feature of EvoluteIQ includes inference management, model registry, and monitoring. Inference offers batch, near real-time, REST API-based monitoring and ensemble capabilities. The Model Registry has staging versus production registries, versioning of models, deployment reviews and approvals, and backup functionalities. Continuous monitoring features cover model performance, inference performance, model parameters, metrics, and overall usage of deployed models.

Redefine Data Horizons with GenIQ

GenIQ presents a unified solution for maximizing data excellence throughout every stage of operations. From preprocessing to model management, it streamlines processes, ensuring efficiency at every turn. With seamless data preprocessing and model development, organizations can customize models efficiently to their needs. The simplified deployment process and model management ensure optimal performance and effectiveness, flaring the full possibilities of data to drive automation innovation. Explore the capabilities of GenIQ today and chart a course towards data-driven dominance.

Sanjay Koppikar

Chief Product Officer, EvoluteIQ
Sanjay Koppikar, Chief Product Officer at EvoluteIQ, leads EIQ's roadmap for our Intelligent Business Automation platform, managing product ideation and development. He oversees engineering and R&D and has a rich entrepreneurial background, including founding Quadwave Consulting. Sanjay excels in BPM, CEP, Big Data solutions, and global technology strategy. An accomplished author and speaker, he inspires with his expertise and motivational insights.

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