The success of artificial intelligence solutions depends on high quantities and quality of data. We provide data augmentation services to improve and expand existing data features. We survey adjacent internal and external data sources to join, creating additional opportunity for a solution development.
If the data you need is for sale, KUNGFU.AI has the relationships to acquire it. We partner with leading data providers and have experience brokering data deals.
Sometimes the data you need is not for sale. We automate structured data collection on the web and package it up using the latest web crawling technologies.
Sometime web crawling is not impossible and human intervention is key. KUNGFU.AI has built a team of hundreds of contract workers, all connected virtually, all tested and qualified, that are waiting for work RIGHT now.
For most businesses, augmentation opportunities are not obvious. Most AI journeys require upfront assessment to understand where data wealth, differentiation, and dependencies exist. Our data strategy assessment will take a deep look at all of the data that is important to your organization. This assessment helps identify gaps, quality issues, augmentation opportunities, and establish a data collection strategy.
One of your most important skills for the future of AI development is selecting the right modeling techniques and algorithms that solve the problem. We develop AI-driven solutions. We favor building from the open source community, applying transfer learning, to see success faster.
We practice agile methodologies that are adapted for artificial intelligence.
These days developing a working model is much easier than it used to be. But making a model an effective solution that solves business problems, that’s where the value is. Stages of product development:
AI is not like typical software engineering. Feature engineer and tuning hyperparameters can be complex and prone to human error or lack of experience. The engineering effort doesn’t always deliver the results you’re looking for. To address these risks, we take the approach of rapid prototyping and test multiple techniques to either get good results or fail fast and re-architect. And we run proof of concept development in short sprints that allows us to make timely, collaborative decisions.
Once the technique is selected and initial acceptable accuracy levels are achieved, we integrate the model into a user-friendly interface and test the solution in a limited, controlled environment. Based on the performance, we evolve the solution, test (function/scalability/supportability), evolve business case, determine change management plans, pilot in location(s).
After a successful pilot, the solution must be adapted for scale. This includes establishing a human-in-the-loop process and deploying it to planned locations; including physical, computing, and business change management.