Your Frequently Asked Questions about visual data, machine learning and AI.
Why 7 Weeks?
The idea was born out of what we’ve seen go wrong time and again, when companies commission consultancies to undertake a piece of AI work. After haemorrhaging cash, there is often nothing to show for it as the main goal is often lost in BRDs, change requests and large teams burning lots. Analytics / Data Science in particular doesn't lend itself to the large consultancy model but we think this works well with a small, agile team of really smart people with tailored tooling (pre-written packages, cloud computing power etc.)
We've always been able to deliver something in 6 or 7 weeks, and we're also cleverly packaging up what we do into re-usable code and application blocks, which really propels a project along and helps us deliver assurance against tight timeframes. We're an agile team, so this usually works because we're focused, and lean enough to deliver results.
Ultimately though, we work with you to understand the process in question, to create an AI model, train the model and deploy it in your internal systems, apps or API.
What happens after 7 weeks?
Follow on is really at our Client's discretion. We're proud to be transparent and open. You will see results within 7 weeks, with functional, usable outputs that improve efficiency. The outputs can be used to build an in-house suite of processes, or you can decide to undertake additional work with Keen AI, to iterate to incrementally improve.
Who and why are Keen AI?
AI is beautiful. As a team, we're all fascinated and excited by neural networks, deep learning and machine vision. The value of AI in improving, enhancing and increasing efficiency is so fantastically untapped, and we're keen to be involved any application that can enhance the human experience. We're driven by the potential, and formed Keen AI to be part of, and hopefully one day, lead the revolution.
You sometimes call yourself a solutions factory. What is a solutions factory?
We developed the KAI platform as a response to a need from one of our clients. It was a happy accident. Our lean, agile approach meant that we could tinker, and re-tinker quickly, efficiently, and without layers of red-tape. Now red-tape isn't a bad thing, but at the innovative level, it can often restrict the speed needed to fail-fast, learn and re-learn, adapt and adapt again. We consider ourselves a solutions factory for that reason. Often, our client's specific requirements do not fit in one box, so we're continually redesigning the box to deliver AI solutions that precisely meet our clients needs.