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We want to understand your business needs.
Our gamut of competencies allows us to become a perfect fit with your organization. We set our sights on joint success, so we play to each organization’s strengths for a unified experience. Fair play and clear division of labour make for easy and reproducible engagement models.
We’re proud of our ability to deliver to milestones in rapid 3-6 week cycles. Once initial projects are completed, we often keep working with our clients to ensure ongoing success. Because we consider the strategic level as well, we can help build out a series of initiatives for the long term.
Before starting an engagement, it’s critical to project success that we help you define the right problem. As part of our consulting services, we will work with you to determine the best potential application of AI for your goals.
We learn about your processes, your KPIs, and your measures for success for the project and for your organization as a whole. Our goal is to help you champion the initiative to your managers, investors and other stakeholders. Understanding the broader context you’re working in allows us to identify any unintentional ethical biases or any data-based impediments to achieving desired project outcomes.
Once the strategic approach is approved, we verify the data, build a model or models around it, and begin testing. Depending on the type of engagement, this phase will include data cleanup, research and development, product design, and machine learning integration steps.
For example, image files can be large, and when thousands are sent in from remote cameras, it takes too long to send them to the server, digest the image, to put them into the machine learning model, and wait for it to analyze and return the results.
Our team’s experience in cybersecurity, medical devices, and defence solutions inspires us to continuously evaluate risk at all levels of development and integration. This vigilance takes practical form in activities like:
Working with partners to design user interfaces where users are provided with the right questions to ask, not given explicit answers.
Following best practices in information technology security to ensure that client data and intellectual property remain secure.
Monitoring solutions post-deployment for resilience and expected performance.
Whether the system will be located on the cloud or on a private network, we use frameworks like TensorFlow, Keras, and PyTorch as needed to speed up development.
Machine learning engineering allows us to bring the data science together with software engineering practices to deliver an optimal model or models for the purpose. While the science tells us how to do something once, the engineering allows us to do it right, in field conditions, and at scale.
We build to ensure future growth in many dimensions, making sure it’s not only expandable in terms of abilities, but it can also scale to serve millions of users.
As we build out our prototype, we start ensuring that it will work as well in the field as it does in our test environment.
For cloud deployments, we build AI systems on AWS, Microsoft Azure, or IBM Cloud. For on-premise deployments, we typically develop on Red Hat or Ubuntu.
Because so much of the work we do is unique to each client, we’ve developed a rigorous process.
Our project management approach is based on software development best practices that account for and reduce project risks. Multiple milestones ensure that you and your team can maintain flexible control over the project’s evolution, and that we stay on top of any new considerations as they surface.
Proactive communication is at the heart of what we do. Maintaining a steady flow of information between all project stakeholders is how we deliver exceptional quality fast. We are equally at home talking to your experts and to managers using terms that make sense to them.
