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AI Platform PowerPoint Pitch Deck

AI Platform PowerPoint Pitch Deck

The management of Altada, an AI platform and IBM partner, applied to me with the request to design their company pitch deck. They were very satisfied with the result.

About Altada:

Enterprise clients have discovered that change management programs esp. those focused around digital transformation and regulation are difficult to manage, monitor and risk control. Attempts to construct solutions around data lakes ingesting structured content from project management and enterprise resource planning systems, using data analytics are falling short in terms of delivering the actionable insights necessary for risk mitigation, practical process automation, and competitive advantage creation. The results are more frequent delays, poor delivery, and human wastage. This leads to the   heighted operational risk and sub-optimal capacity utilization and the cost hundreds of millions and billions of euros per enterprise.
An AI Planning framework methodology to deliver operational AI solutions. Altada’s operational AI solutions encompass intelligent document automation and robotic process automation. They are developed and deployed alongside predictive models for capacity utilization, resource planning, and revenue management. The AI solutions, based on both machine learning and deep learning principles are produced to:
Augment data analytics by more rapidly and accurately identifying risk patterns emerging in change management programs. These patterns are often hidden in unstructured data sets.
Evaluate existing supply chains, identifying new automation opportunities can be introduced through classification, transformation, and extraction. Often this involves capturing and processing data lineage in a new way.
Altada’s solution are deployed as both API services, as well as full software applications. They can be developed rapidly and are delivered to manage all the key operational risk areas that plague change management programs. They reduce delays, improve delivery quality, and reduce risk through automation; they help enterprises grow their operational capacity, achieving more scale and reduced cost.

The Altada AI platform is continuing to evolve with the advancement of AI Research. We have already created a rich set of components that can rapidly and accurately classify, categorize and validate data. We deploy these data intelligence techniques to ensure our clients acquire AI models that satisfy the stringent criteria of privacy by design. We exploit both supervised and unsupervised learning techniques to train our models. We use a multi-thread/parallel processing approach to enable competition between AI agents, and constant optimization. We are a firm that strongly believes that putting a human in the loop in the early stages of AI modelling will lead to greater early accuracy, which can then be exploited by self-learning techniques. Harnessing this combined approach for text analysis and computer vision problems has helped us achieve machine reading accuracy in the mid 90’s consistently. Our underlying modelling framework has been designed to maximize model selection opportunity. We believe there is no right answer when it comes to model selection, and thus exploit both machine learning and deep learning models in our delivery. We have built and deployed solutions, in the last 2 years for example, using knowledge graphs, Random Forests, Probabilistic Inference and Decision Trees. Our leadership for the past 20 years have been strong proponents of constraint programming, AI planning and learning optimization. These techniques allow us to both reduce time to production, as well as assist our clients in future proofing their AI powered applications.

Altada builds Operational transformative solution via APIs and Applications by:

  • Demonstrating through rapid prototyping its ability to provide value to a significant operational challenge.

  • Developing Proof of Concepts (POC) that can rapidly be transformed into production ready solution. This is achieved by testing solutions with real world data sets, and in conditions that reflect deployable infrastructure and integration services.

  • Working across the full operational supply chain with its clients. This allows the company to discover new connected use cases, and to augment its existing solution set in a logical way and within an increasingly automated design.

 

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