In a data driven world, building Advanced Analytics and AI/ML capability is your innovation powerhouse, and setting it up requires time and monetary investment in identifying and hiring skilled talent. It is a culmination of months and years of bringing in multi-disciplinary skills which requires deep understanding the talent market and getting the right mix of the team to perform at a high level very quickly. This is where we come in.

We help you build high-quality Advanced Analytics and AI / ML solutions tailored to your business needs through a small team of highly-skilled individuals, in a matter of weeks, and not years. Our approach to building such solutions rests on our capability to establish Centers of Excellence (CoEs) in Artificial Intelligence, Machine Learning and Data Science that are backed by our Business Backward Approach to solving business problems.

Our approach towards setting up an Data & AI CoE for you is built on three key parameters

• Building requisite skills
• Focus on innovation
• Governance

Building Requisite Skills

The successful implementation of Artificial Intelligence (AI) systems requires a combination of skills in data science – deep understanding of mathematical modelling & statistics, engineering, and business. It’s not just the person, but also the platforms that come into the picture here.

A data scientist is crucial for dealing with exploration and analysis of large volumes of data to extract meaningful insights and build predictive models. These data scientists should possess an understanding of business context and KPIs to develop meaningful models that value add to business. For this, they require an analytical data platform.

An analytical data platform is the foundation of modelling and requires core engineering skills. Data engineers ensure that the AI system has access to clean, structured, and well-organized data by building robust data pipelines and maintaining efficient data infrastructure. This leads us to software engineering skills.

Software engineering is required to integrate data science models into business systems and thus make the recommendations utilizable. If you look at the larger picture, the implementation of an AI system requires the data scientists, data platforms, data engineers, and software engineering to work in tandem with each other, keeping the business context as the guiding waypoint.

A CoE achieves this objective by bringing in such multidisciplinary team. By combining the skills of data science, data engineering, and business, multi-disciplinary teams can effectively implement effective AI solutions. The collaboration between these disciplines ensures that AI models are trained on relevant data, data pipelines are robust and efficient, and AI systems are aligned with business objectives. The synergy of these skills maximizes the potential of AI and leads to successful implementation in real-world scenarios.

Focus on Innovation
The field of AI is changing rapidly with advances coming in every week or at times, every day. We work with you on innovation and rapid experimentation at the CoE. When building something new, you can rely on us for our technological and business expertise to create smaller experiments that come together to build something that you can take to market with ease. Our CoEs provide the ideal playground for your business to explore new ideas and push boundaries.

By bringing in the collective experience of working with multiple clients and being at the edge of innovation, we are able to effectively leverage this knowledge to deliver a quick turnaround for your business needs.

Governance
The Prescience CoE team will be tightly aligned to your organization at every level. Metrics and measurements will be defined at a work-stream level. A Prescience Founder will be directly involved in providing leadership & oversight to the CoE to ensure quality & priority commitment.

The Governance aspect for the CoE will focus on the following strategic parameters: