Our process will focus on your business goals

Counseling

At our company, we are committed to offering comprehensive guidance to ensure our customers embark on a seamless journey when integrating AI systems into their business operations. Our primary objective is to address a wide spectrum of quality, functionality, and intellectual property considerations when it comes to AI solutions. Some of the critical focal points in our customer consultation include:

• Intellectual Property Strategy: We assist in identifying the most suitable property rights, publication, and patent policies tailored to your specific needs
  
  
• Digital Transformation Requirements: We provide expert advice on the digitalization requirements of your AI project, ensuring that it aligns perfectly with your business goals
  

• Algorithm Selection: We help you choose the most appropriate AI algorithms that meet all the criteria and constraints related to functionality, data availability, output accuracy, consistency, explainability of results, training time relative to computational resources, and timeliness of results
  
  
• Infrastructure Selection: We prioritize your needs and budget when selecting the optimal computing infrastructure. Tensor Bridge remains unbiased, without any allegiance or sales agreements to any infrastructure providers. We're dedicated to choosing what's best for our customers. We are continuously testing and working with a large spectrum of infrastructure solutions (Cloud computing, TPU, GPU, multiprocessors, FPGA, in different combinations and configurations)
  
  
• User Experience and Training: We focus on ensuring your staff's proficiency and ease of use by following industry norms and standards, creating interfaces that users are already familiar with
  
  
• Maintenance and Support: We establish clear procedures for system maintenance, fault detection, and recovery, ensuring your AI system runs smoothly at all times
  
  
• Traceability and Reporting: We guarantee full traceability and reporting for all AI system computations and decisions, allowing you to monitor and understand the system's actions
  
  
• Regulatory Compliance: We ensure that your AI solution meets all necessary regulations, classifications, and standards, ensuring that your product is fully compliant
  
  

At Tensor Bridge, our commitment is to empower your business with AI solutions that are not only cutting-edge but also tailored to your unique needs. We're here to guide you every step of the way on your AI journey.

Evaluation

An AI product undergoes evaluation at several key phases of its lifecycle, including (1) during the pre-study phase, (2) during development, (3) at the point of delivery, and (4) after a period of use. Early problem detection is crucial for cost-effectiveness. The product evaluation encompasses a few different components, with the results documented in an Evaluation Report.

A well-structured evaluation process ensures that the AI product not only functions effectively but also aligns with user needs and remains reliable and secure throughout its lifecycle.

Prestudy

The prestudy has three overall goals:

1. To define the business case: stakeholders, expected returns, market and competition
   
   
2. To define the extend of the project in terms of technology, algorithms, staffing and costs
   
   
3. To define success criteria for the project
   
   

A prestudy for an AI project is a critical phase that helps set the stage for a successful project. It involves gathering information and conducting an initial analysis to determine the feasibility, scope, and requirements of the AI project. Here are some detailed points typically included in a pre-study for an AI project.

The output from the prestudy are typically (1) a Pre-study Report, (2) Excel file with the cost-benefit analysis, (3) Sometimes a prototype.

A thorough pre-study helps ensure that the AI project is well-planned and aligned with the organization's goals, increasing the likelihood of a successful implementation.

Identify
precondition

When considering the use of AI, people often wonder whether a particular algorithm qualifies as AI or if a specific problem can be solved using AI. Defining what constitutes an AI algorithm is not straightforward. Deep Learning is widely accepted as a clear example of AI, but some algorithms fall into a gray area. Algorithms in this gray zone include rule-based systems, genetic algorithms, natural language processing, clustering, and expert systems. This ambiguity arises because AI encompasses a broad range of algorithms, and what was once considered AI may now be seen as simpler applications based on concepts like probability theory.

What's more important is understanding the properties of practical problems that determine their classification. For instance:

• Regression problems deal with continuous output
  
  
• Classification problems involve discrete outputs from a defined set
  
  
• The presence of states and whether they can be represented in a graph
  
  
• The nature of the problem, whether it's text-based or image-based
  
  
• Whether the problem is dynamic
  
  
• Whether it's an optimization problem or involves complex searches in a vast solution space, among other factors

AI experts are trained to classify problems and match them to appropriate algorithms. The resulting algorithms can vary in complexity, depending on the problem. A critical differentiation in solutions is whether they are parametric or non-parametric. In the former, a training set is converted into a numerical set, typically represented as a tensor. In the latter, a set of relevant training data is retained.

However, even seemingly simple AI systems become complex when operational. An AI application acts as a central hub for managing information from a large training dataset, user queries, various parameters, configuration files, and user-generated data. Each of these components comes with its own set of assumptions and preconditions to function correctly. During the design phase, these preconditions may be apparent and integrated into the system by developers. But over time, these preconditions can become less evident, especially when the user base changes or the system undergoes updates. Therefore, it's crucial to document all pre-conditions, assumptions, and problem classifications digitally as part of the system's current version to ensure its continued functionality and maintainability.