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 into the customer’s business environment.
We cooperate with businesses of all sizes, from startups to large enterprises. Our experience tells us that, regardless of the size of a company, business owners face similar challenges when 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, advisors, 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, and predictive analytics.
ML may not be the key expertise of your company. In this case, we work with your existing engineering team and provide the API of an ML system that fully corresponds to your needs and requirements. This way, your team can focus on their primary tasks rather than trying to learn an entirely new discipline.
This type of cooperation often starts with the development of 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 to break down these goals into logical steps, define and prioritize use cases, and provide the best solution for each to achieve complex results.
The client: a debt collection agency operating within various industries.
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 the ability to optimize collection agents’ time, allowing them to target the most promising accounts first.
The InData Labs team worked on the implementation of a neural network to improve irregular cycle prediction for users of the Flo application.
The current version of Flo’s neural network implemented by InData Labs can improve irregular cycle prediction 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. The InData Labs team built a customer feedback tool that allows timely download and analysis of all the reviews from YouTube and forums.
Our solution was powered by custom NLP models trained to understand the terminology and slang of the particular game.
With the help of the tool, the client’s team now has all the necessary information at hand as soon as the new release goes live and can react to their players’ feedback as quickly as possible, avoiding a complex review retrieval process.
The client required a recommendation engine that would offer up-to-date, personalized movie recommendations for every single user, thus improving customer experience and driving more sales from the recommendation page.
Our team used machine learning to build a custom recommendation engine that makes individual recommendations to millions of users.