AI Augmented Enterprise hyper automation engine
Large Language Model (GPT-3) based NLP engine for descriptive and predictive analytics for enterprise use cases
hyper automation for enterprises
- Natural language interface
- Formal model of the enterprise processes
- Descriptive analytics
- Action commands
- Deep actionable insights
- Free Desktop app for Test Automation
- Supports Web, Mobile and API Testing
- Supported on Windows 10 and Mac OS
- Analytics and Error classifications
- Limited Support
- Ability to query underlying platform in natural language
- Actions as well as query commands
- Domain specific automated learning
- Enterprise domain
- Portable to different type of enterprise systems
Ability to interact with software development and ERP systems in a natural language will provide immense benefits to the stakeholders. However, so far this has proven to be extremely challenging with limited success in narrow applications. However this is changing significantly since the fast emerging approaches of Large Language Models (LLM) and Artificial General Intelligence (AGI). Atalgo has been spearheading a research project to establish the use of LLMs in the specific domain of enterprise software development. We hypothesize that if LLMs are trained for Software Development Life Cycle (SDLC) models, we can develop an effective Natural Language Processing (NLP) layer to have bi-directional communication with underlying systems. These systems could be standalone test automation tool or a host of toolsets as part of entire SDLC.
Phase 0 – This phase is about doing initial experiments and developing a proof of concept of Flame architecture on top of our own proprietary test automation framework. This framework is a desktop application to conduct automated functional testing for Web as well as Mobile apps. Atalgo’s NLP engine and Query Execution engine is built on top of this automation framework.
Current capabilities include querying the test automation framework (for example, “fetch the Test Summary Report for last execution”) and providing instructions (for example “schedule an Order Management test run on UAT tomorrow morning”). As of Nov’22, the Flame hyper automation engine is working with initial results on the test automation framework and can be demoed.
Phase 1 – Phase 1 is where Flame AI capabilities will be expanded to a complete SDLC toolsets and will NOT be limited to only test automation framework. Enterprise IT project ecosystem is complex and ability to interact with it in natural language in real time will provide immense opportunities and actionable insights to stakeholders. In phase 1, we will strive to support most common architectures and key parts of SDLC ecosystem while seeking valuable feedback from early adopters. As per current roadmap Flame engine will support both descriptive and instructional commands.
Phase 2 – Once proven across multiple toolset ecosystems and with a mature Flame ontology, we will strive to expand this offering all type of commonly used ERP systems and towards predictive analytics. With a built-in Business Intelligence (BI) layer, Flame will not only be able to answer strategic questions about a business, but also help visualize it.
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