Our Approach

We work to provide superb and relatable solutions to new and existing clients, understanding your challenges and goals

Stages we follow

Definition & Discovery

This is the initial stage of every project where we identify and define the project’s goals and objectives. During this phase, we collaborate with clients to collect data on their organization, goals, barriers, resources, and present position.

Design

We use the information gotten from the discovery phase to make choices on how your solution should be designed. In this phase, we aim to develop one or more designs that can be used to achieve the desired project goals. Stakeholders can then choose the best design for the execution of the project.

Development

During the development phase, everything needed to carry out the project is arranged. We make a schedule, materials and tools are ordered, instructions are given to the personnel, and so forth. This phase is complete when testing or deployment is ready to begin. All matters must be clear for the parties that will carry out the testing. The important point is that it must be clear what must be done in the implementation phase, by whom and when.

Testing

Once development is complete, we begin a series of activities crafted to investigate the progress of a particular project and provide stakeholders with information about the actual performance and quality of the project. We attempt to provide an independent view of the project so that stakeholders can assess and understand the potential risk of project failure or nonconformity.

Deployment

After passing the testing phase, the solution is ready to be used in a real environment by the end-users. Our team implements the programming and coding and ensures its availability in each system location or region.

Support

Even after deployment, we help make sure the solution is up to date and provide assistance when needed.

Case Studies

Genpact / PVAI (Pfizer, GSK, Bayer) (Machine Learning Model Infrastructure Review)

Background

PVAI was incubated inside of Genpact to construct a flexible, verified collection of top-tier Artificial
Intelligence SaaS modules, tailored exclusively to address the various challenges present in
Pharmacovigilance (PV). These SaaS modules licences were sold to Pfizer, GSK, and Bayer and
each clients backend infrastructure and application were hosted in its own AZURE / AWS account.
JPhozin was assigned the to review (AI) and ML services, infrastructure, and implementation
resources and recommend enhancement of the ML models automation with cost optimisation
factored.

How Jphozin handled the project

When it comes to evaluating and enhancing the infrastructure that supports your machine learning models conducting a Machine Learning Model Infrastructure Review is essential. This step is crucial, in guaranteeing the dependability, scalability and efficiency of your ML applications. Here’s a guide, on how Jphozin tackles this process;

Define Objectives and Scope

Defining clear objectives and scope is the foundational pillar of a successful infrastructure review.

Step 1

Gather Information

In the process of conducting a Machine Learning Model Infrastructure Review, gathering comprehensive and accurate information is the cornerstone of success.

Step 2

Develop and Conduct Assessment

To do this, We scrutinize performance, scalability, security, and cost-efficiency, striving for optimal result. With a focus on documentation, automation, and adherence to industry best practices

Step 3

Prioritize Gaps

This is pivotal for ensuring the optimal performance and reliability of your systems. By identifying and addressing these gaps, you can streamline your model deployment, enhance security, and reduce operational costs.

Step 4

Develop Remediation Plan

it is essential to develop a robust Remediation Plan. A plan that should serve as a clear roadmap for addressing any identified issues and optimizing your machine learning infrastructure.

Step 5

Documentation and Reporting

Our reporting system provides a clear and concise summary of the review's findings and recommended improvements. This enables us to make data-driven decisions.

Step 6

How did project benefit

After undergoing our comprehensive assessment of the clients machine learning infrastructure, we were able to identify and rectify existing bottlenecks, streamline processes, and unlock tremendous potential for growth. We helped to enhance (AI) and ML models infrastructure automation and drove down monthly cost by 30%. We identified issues with the immature deployment process and helped in redesigning of the deployment automation which reduced deployment of 350 environments from 2 weeks to 20mins.

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