Flame as an Intelligent Knowledge Engineering Platform

Flame is flagship AI platform developed by Atalgo Engineering to enable enterprises to utilize computational intelligence to solve complex business problems. The Flame’s philosophy is to build a computational model of an enterprise as an operating environment for technical and business process to be AI augmented. This is a massive undertaking and our our approach is iterative. In first iteration, we want to build a platform where organizations can build a Knowledge Base of the various processes. We are calling this Intelligent Knowledge Engineering (iKE). In second phase, we will enable our users to develop and deploy intelligent applications to do various tasks more autonomously, efficiently and with minimal supervision. The platform will learn as it will be used across various functions within an more

Flame – AI Augmented Problem Solver

Flame as a world changing AI augmented problem solver is expected to work at the level where it can help enterprises and individuals solve complex problems using machine learning and intelligent knowledge engineering. The vision of Flame involves behaving like an interactive problem solver capable of taking inputs from domain experts and capable of generating heuristics related to the problem at hand. As a secure and compliant problem solver, we should be able to control what information Flame can have access to (for example whether it can refer to internet or not) to be able to solve the problem... read more

Flame – Enterprise AI Platform

Flame is an AI platform for enterprises. This is an ambitious project undertaken by Atalgo Engineering team where we are aspiring to model all the processes of an enterprise on a single platform and build a comprehensive knowledge base of the organization which can be exploited by Generative AI to automate and smarten various tasks.At high level, we see an enterprise comprised of two types of processes – Business and Technical. Examples of business process are Account Creation, Reconciliation, Fulfillment etc. where examples of technical process are Code Development, Testing, Deployment, System Maintenance etc... read more

Enterprise Applications of Generative AI – A Perspective

Generative AI has created lot of excitement in the industry and AI research community in past few months. We have seen explosion of various SaaS products and services as a result of the exposure of GPT-3 and other products from OpenAI to general public. This has allowed experts as well as average curious person to explore the possibilities and come up with new ideas of how these technologies can be used in various ways to enhance the human knowledge and capabilities. I think the business case for generative AI applications is slightly more clear from standalone consumer’s perspective than as an enterprise usage.... read more

Multidimensional complexity of Enterprise Product Engineering

My experience with regards to enterprise product engineering is that the main reason for the complexity in such projects is that enterprise product engineering is a multi-dimensional problem. The implementation plan needs to take multiple dimensions of the requirements into account. This is seldom planned in detail in the early stages of the project and we have to face the consequences in subsequent stages.... read more

APEM – Atalgo Product Engineering Methodology

Enterprise product engineering is a complex beast. It involves navigating in a very complex ecosystem of various tech stacks, stakeholders and different concurrently running projects. Also, there is a huge cultural dimension to the delivery of any successful enterprise project and this is also driven by complex legacy procurement and contract processes.I have been navigating this space for quite some time now; and so has my team at Atalgo. We developed extensive knowledge and experience in enterprise software delivery space while managing added complexity of employing offshore development team and learnt a couple of things along the way. This further strengthened our consulting practice and allowed us to abstract the pitfalls and learnings of enterprise software delivery. This applied learning is what I am calling APEM, Atalgo Product Engineering Methodology. read more

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