We are a team of skilled and talented engineers who have been working professionally with data for many years.
Analysis, modeling, processing, data science and operating the systems that we create is what we live and work for. And if we ever need a tool to improve the work we do, we develop it ourselves (Nestlogic ADP is a prime example).
Something is broken in the US healthcare billing. Each year state healthcare expenses grow, and billing itself became more complex.
It’s not a problem for big hospital systems to hire lawyers to negotiate new contracts with insurance companies in this changing environment to keep hospitals profitable. But for some hospitals, it’s not so easy to get a good contract.
And here’s where we go on the stage.
Our goal is to help hospitals during the contract negotiation process with insurance companies to get a contract that is acceptable by an insurer and profitable for a hospital.
Our approach is data-driven. We use hospital historical data to model the new contract.
At the same time, the legislation evolves. For us, it means that so should do our process. So we meet new challenges periodically changing the modeling process. It involves growing our expertise in all the fields around healthcare.
But things do not stop there. We’re still looking for opportunities to improve our project. As the approach is data-driven, it means we’re aiming to collect and process more relevant data. Some data comes from open sources with all the consequences: it’s dirty and hard to process. So there are some challenges in the field of data collection either.
We are building a PaaS solution working in a client’s cloud environment. It’s a smart virtual analyst with NLU interface over BI of any complexity needed for the organization.
“Smart” is the key feature because the analysts’ skills are dynamic and easy-maintainable.
Product uses AI and trains models for phrase detection and slot filling. The solution includes productization of jupyter notebooks as “skills”, usage of business ontologies for data gathering by skills.
We provide generic all-in-one solution for sales, marketing, logistics, etc. Client receives ready-to-use virtual analyst with the chatbot interface integrated to a variety messengers. More skills are implemented by your analytics — smarter is analyst. The skills are easy to come with: starting with few lines of python code you may produce anything that might come handy — graphs, metrics, predictions and insights.
Most of the platform is done in serverless terms. Supported clouds are AWS and Azure, at this time. Most of the components make use of lambdas (function apps), API getaways (API management), sqs (service bus), ecs (aks), DynamoDB (cosmos dB), s3 (blob storage), etc. Dialog capabilities are closely integrated with Amazon Lex service. Jupyter notebooks are productized with docker containers and shipped to execution facility. Infrastructure is able to connect to variety of messengers, e.g., telegram, WhatsApp, teams, email.
Since platform is mostly used for internal company purposes, the accessible data is private and it’s being secured, and additional authorization is applied on messenger layers.
STEALTH MODE STARTUP
Financial Markets are a place where people tend to earn and lose a lot of money. In recent years cryptocurrency markets have been rapidly developing. For example, the very first and the most famous crypto “Bitcoin” has grown in price since its inception from few dollars in 2011 to 40 thousand dollars nowadays.
Automatic trading is significant on financial markets. Algorithmic trading contributed nearly
That’s why the strongest data science team in Nestlogic dedicates efforts to build most sophisticated models for trading algorithms in cryptocurrency markets. We integrate most of available financial data in the world in the attempt to build alpha strategies. As professionals in Data and ML ops we’re using the best available solutions, modern cloud services and big data tools. The toolbox consists of but not limited to Apache Spark, Kafka, Vertica, various AWS services to store and process data as well as for integration with external services and systems.
Have you ever thought about game making decisions based on your actions?
Our game collects and analyzes data about a player’s actions and behavior in real time so it has no trouble taking the right actions or making appropriate decisions.
Progamero uses the power of real-time personalization to enhance the player experience. Progamero creates a template based on the data markup as a convenient starting point for the task. Additionally, a data scientist should continue the development of the AI Bundle. Technologically, the AI Bundle is a Python-language code that implements a specific interface. Progamero has practically no limitations and can range from simple rules to complex neural networks, all of which can be used inside the AI Bundle.
Offline uses a simulation based on historical data. For example, we can find out what this AI Bundle would have done yesterday using yesterday’s data. The system enables the dynamic selection of a group of players, and it can associate the group with any AI Bundle in order to measure the effect of its actions. Progamero can measure the basic KPI, so there’s no need for an external analyst to assess the results.
Many game developers don’t even offer game personalization because the threshold to enter the field is so high.