Your AI journey is distinct, and we follow proven processes to ensure organization buy-in and quick deployment.
Assess the opportunity.
We start by understanding your industry and business goals. With this insight, our team works hand-in-hand with your team to identify the opportunity, prioritize features, estimate returns on investment, and leverage insights from senior leadership.
Identify the relevant data.
The strength of your data determines the success of your AI product. We begin with a data maturity assessment to understand the type, amount, age, and frequency of available data. This process identifies what additional data gathering tools are required to maximize your product’s impact.
Walk the shop floor.
Understanding end users and eliminating pain points is critical to ensure adoption. We create detailed user stories to fully understand end users and create a vision for how AI enabled products will improve their work. Together we quickly design low-fidelity prototypes to ensure a functional, intuitive, and useful UI/UX they are excited to use.
Do the math.
We employ professors from top universities and experienced data scientists to create the best algorithms given the data infrastructure available. We are experts both in machine learning (e.g., predictions, recommendations, and explorations) and foundational techniques such as operations research and optimization.
Build your product.
Our developers build bespoke AI products for you on free and open source libraries which are often years ahead of legacy tools. Additionally, you have full control over feature prioritization and are no longer bound by off the shelf software constraints. We work in sprints to drive speed and push finished products for immediate feedback and continuous improvement.
Together we create training materials including written documentation, in app feature walk throughs, video tutorials, and detailed FAQ to drive adoption. During deployment, we fix bugs in hours, and improve the user experience the next day. We track adoption metrics and conduct user interviews to understand pain points and identify new features.
Secure your investment.
We take data security seriously and assume threats come from anywhere at anytime. Data security is achieved through a clear cyber security policy, an understanding of threat models, and high test coverage in our DevOps pipeline. We design mechanisms where everything is versioned and time stamped, and every merge request is signed off as a team to reduce risk.
Deploy, maintain and improve.
We continuously monitor and improve the performance of your AI through data pipeline maintenance, edge case testing, and applying Machine Learning techniques, such as active learning. There will be feature improvement requests from internal users and customers. We work together to identify opportunities and prioritize work to drive user adoption.