We use machine learning (ML) tools and algorithms to help companies develop AI-driven products and solutions. Our team has profound knowledge and experience in designing, implementing, and integrating artificial intelligence solutions within the customer’s business environment.
We cooperate with businesses of different sizes, from startups to large enterprises. From our experience, we know that regardless of the size of a company, business owners face similar challenges planning new data initiatives.
Our clients either don’t have their own data science teams, or their team is too small to cope with all the tasks in the environment of a fast-growing company.
With InData Labs, you’ll save time on looking for top-notch specialists. Our team of world-class data scientists, AI consultants, and ML engineers will bring technical know-how to your project from day one.
If a company already has a data science team, we become a valuable asset bringing profound expertise in certain subfields of AI, such as computer vision, natural language processing, or predictive analytics.
ML may not be the key expertise of your company. In this case, we are working with your existing engineering team. We provide the API of an ML system that fully corresponds to your needs and requirements. This way, we allow your team to focus on their primary tasks rather than try to learn an entirely new discipline.
This type of cooperation often starts with developing a proof of concept for a business unit. Typically stakeholders are non-technical people who have very “high-level” goals. We work closely with them breaking down these goals into logical steps, defining and prioritizing use cases, and providing the best solution for each to achieve complex results.
The Client is a debt collection agency that collects debts across various industries and customers.
Our challenge was to improve debt collection effectiveness with the help of predictive analytics.
As a result we improved the efficiency of the debt collection process and gave the Client an ability to optimize collection agents’ time, allowing them to target the most promising accounts first.
InData Labs’ team was working over the implementation of a neural network to improve irregular cycle predictions for application users of FLO.
The current version of Flo’s neural network implemented by InData Labs can improve irregular cycle predictions by up to 54.2% depending on the quality of input data, with prediction error reduced from 5.6 to 2.6 days.
The Client – an international game developer and publisher.
InData Labs team built a customer feedback tool that allowed to timely download and analyze all the reviews from YouTube and forums.
Our solution was powered by custom NLP models trained to understand the slang of the particular game. With the help of the tool, the Client’s team has all the necessary information at hand as soon as the new release goes live and can react to their players’ feedback as fast as possible avoiding a complex review retrieval process.