NeuroSoph’s approach for each AI engagement is based on a 7-step process:

Use Case

NeuroSoph determines if the given use case can be solved using AI. If the use case can be solved, we proceed with the use case. If not, we let our clients know that the given use case not be solved by AI and let them know why (i.e. advancements in AI are not ready for the use case).

Business Understanding

The objective for all the stakeholders is to understand the business problem to be solved. Framing a business problem in terms of expected outcome allows NeuroSoph to recommend the proper AI solution.

Data Understanding

Data is the available “raw material” from which the AI solution will be built. In order to deliver an effective AI solution, it is important for NeuroSoph to understand the availability of data, strengths of data, and limitations of data.

Data Preparation

Includes loading client data into a suitable environment and prepare it for machine learning use. To do this we may need to label data and determine the features, identify data imbalances to eliminate biases, convert it into different formats, and normalize the data. Additionally, we will split the data into two unbiased data sets one for training the AI system and one for evaluating the performance of the AI system.


Choosing the correct model for the business problem at hand for example, there are specific models for image data, working with numerical data, and text data. NeuroSoph’s data science team will recommend the best model to solve the business problem.

Machine Learning Technique and Training (Development)

Machine learning allows a computer program to learn from data without relying on rules-based programming and enables the program to continuously learn. Machine learning generally uses two types of techniques supervised learning which trains a model on a known input and output data so that it can predict future outputs and unsupervised learning which finds hidden patterns or intrinsic structures in input data. NeuroSoph will select the best machine learning technique for the business problem. 

To train the AI system we will use the training data to incrementally improve our models performance. If the output of the model does not match the desired output than we “fine-tune” our model and go through another iteration of training until we achieve the desired results.


The purpose of the evaluation stage is to assess the performance of the trained model using our evaluation dataset and to ensure that the business objectives are being met. This allows us to evaluate the model against data that it has not yet seen, it is a good indicator of how the model will perform in the “real-world”. 

Developing an AI system requires a stream of data, the exploration of data, and constant iteration. After the first iteration NeuroSoph’s data science team knows much more about how the data can address the business problem. That way the next iteration will be much more well-informed and closer to the target state. It is important for all stakeholders to approve of the model’s performance before moving anything into a production environment.

Deployment (ROI)

The AI solution is ready to be deployed into production. The AI solution can be deployed into an existing system, standalone, or integrated into an existing business process. Most importantly it is time for the organization to realize the return on investment.

Lessons Learned

NeuroSoph documents all lessons learned throughout a project and shares the details with clients.  The lessons learned reflect both the positive and negative experiences of a project, this way on the next iteration or project we know what works well and what to avoid.