Enterprise AI Framework

Atalgo Enterprise AI framework enables organizations build a computational model of their business and technical processes using Generative AI stack

Atalgo’s approach to Enterprise AI is significantly different from some other Vendors and Services providers. Instead of just encouraging enterprises to use available cloud-based AI toolsets (such as GitHub Copilot), we propose to build a computational model of the enterprise itself by exploiting Generative AI capabilities of our proprietary platform, Flame. This enables the businesses to build Knowledge Artefacts on which the AI workflows can reason and learn from. This is an iterative approach with built in safety and feedback mechanisms which allows enterprises to keep enhancing their AI capabilities. The model that is built with these fundamentals allows the end users to become more efficient and effective in their work and empowers them to build further intelligent applications on top of the foundations. The reasoning capabilities can be guided and fenced using the platform features. This can also be viewed as hyper automation initiative based on Generative AI stack.


It’s important to start an AI implementation with a strong business case where we can showcase value in terms of gains in efficiency and effectiveness. This is not apparent in enterprises and requires careful exploration and consideration. Discovery phase is to determine the use cases for the uptake of AI Augmentation and assess the readiness of the team. We also look at the riskiest assumption tests, technical and data related requirements as part of Discovery phase. We will develop benchmarks for successful implementation and highlight implementation risks and mitigation plans


Atalgo Knowledge Engineering framework allows organizations to develop a computational representation of the knowledge artefacts and how employees and end users us those knowledge artefacts to complete various business processes. These knowledge artefacts are facts, internal and external documents, links to various resources, toolsets that are used within organizations as well as data captured during the usage of various IT systems. Our proprietary enterprise platform, Flame, allows an organization to engineer knowledge starting with one process and scale.


Technical pipeline is the backbone of Enterprise AI implementation. We propose to use our proprietary AI platform, Flame, to develop and support the technical pipeline which will provide computational intelligence to support the fulfillment of a business process. Technical pipeline is combination and interplay of data ingestion, training, knowledge engineering, testing and monitoring the output for future improvements.


Atalgo framework builds the tool to measure the effectiveness and efficiency of the AI pipeline via agreed benchmarks and metrics. We will measure the effectiveness, accuracy, safety, security, RoI and maintenance cost as part of our AI approach. We will review the outcome with business and jointly explore the areas for improvement, which could be finetuning of our AI model, using different toolsets, enhancing Knowledge Bases or changing the requirements. The findings will be used to improve the approach for next iteration of development as well.


Once the Atalgo AI framework has been proven successful for Discovery phase scope, we will develop a template to scale and expand the capabilities to other business processes and use cases. Also, this will be an opportunity for businesses to transition some of the capabilities in BAU whereas they can learn and leverage from the new technology more effectively. We will strive to make the framework extensible and repeatable across the organization

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Level 1, 191 St Georges Terrace,

Perth WA 6000, Australia




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