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AI can improve over time with better quality data. We ask ourselves initially, how bad can the algorithms be to still achieve business outcomes and provide expected user experiences.
What is the value we are seeking?
What's the best way to bring it into this world?
How tolerant will users be to errors?
Whether you are a small startup or a large corporation, we love to here your big and ambitious plans for AI.
Our goal is to break down your vision down into the smallest possible deliverables with a clear business goal in mind. This makes it easier to find the best way to start and create value.
Back in 2018, Gartner reported as many as 85% of AI projects fail. While we are helping to improve that statistic, there are still many projects that stay in the lab and don't reach production, let alone achieve ROI.
Not doing anything until the data is "clean"
Trying to make the model perfect too soon
Not testing early in the real world
What many providers won't tell you is that validation in machine learning is an iterative process. Next to model accuracy one must look at performance, operability, cost or if we are meeting expected user experience. The importance of each changes in various phases of the development.
We are strongly focused on knowledge sharing because we believe that it's the best way that the technology we love can grow and do amazing things.
Some predict that it will be as common as the relational database, practically meaning it will be in every piece of software we write. Leading companies know that you need to bring business innovation together with the AI technology. So how do we do that?
We bring to your project an expetional team with the right skillsets for each stage of development process. Plus they are generally really cool people to hang around.
Customers typically have a hard time fully explaining themselves. Deep understanding of pains and gains is essential. We go as far as testing the experience ourselves.
With over 50 projects in 12 industries, we know our work doesn't end with delivery. Through education and knowledge sharing, we give you the power to sustain and even improve the solution over time.
We love big ideas, but to succeed with a large scale AI project you need move forward in small steps, considering both the model performance and the way you bring those results into the real world.
A construction site is a constantly changing environment. How can you efficiently monitor it? How can it help reduce injuries? An AI model alone doesn't solve the problem. These questions eventually lead to building a full blown product.
Autonomous drones included :)
We created Kapnetix.ai which makes the mocap process more than 10x faster without compromising on quality.
Our application makes running an international fast food chain easier with realtime insights and recommendations