AI Strategy

Bring business strategy together with the latest AI/ML technologies and best practices, to help deliver leading-edge AI-powered solutions.

We guide your team to a successful AI Minimum Viable Product (MVP)— focused on the right AI use cases that can show return and value.

We engage executive decision makers, product and domain experts, data scientists, data engineers, software engineergs, and machine learning ops teams to arrive at your AI strategy.

Our process includes an AI assessment, as well workshops in data valuation and other topics to get everyone up to speed on AI/ML appoaches.

We go deep into your product data streams and look for the value of the signal in your existing 1st party data, and we also validate other relevant 3rd party data sources.

Once we've completed our AI assessment, determined the value of your data, and delved into your product strategy, we work with your leadership team to identify the optimal AI-powered scenarios and use cases.

We facilitate a process where the product team works together with decision makers to help craft your new AI direction.

After defining your AI MVP, we work alongside industry-leading engineering teams (internal or external) to develop and deploy your AI-powered MVP and platform.

Here's our process to guide you to an AI-powered product.

Understand the business problem. Identify the best AI use cases.

Define the business problem to be solved with AI

Understand the business value for AI

Identify where the data intensity and value are highest

Scope the user journey and operational data flow

Determine the prediction variables. Scope the right AI features.

Determine the key variables to be predicted and optimized

Identify the highest-value use cases for the product

Scope how these will be enabled in the product

Determine the AI features that we will use to predict the outcome

Confirm these features are readily available in the data

Review latest solutions & literature.

Review the latest best practices and solutions available now for our chosen use cases

Determine approaches that we expect to evaluate

Define AI model & training approach. Find signal in the data.

Determine patterns in the data—Unsupervised learning, Supervised learning, or Reinforcement learning

Create hypotheses that we can test in the experiments

Determine the measure of success

Create our evaluation methodology

Scope your AI responsibility and risk management.

Determine the data governance strategy

Build plan to ensure privacy for customers


Ebook | Signal Data Exploration

Getting your product ready for AI.
6 Steps to find signal in your data.

Get the Ebook > 

Enhance your product with AI.
Develop your AI MVP.

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