Perfectial helps businesses meet their needs every day, building smart applications able to collect, analyze, and realize billions of data records from different sources (e.g. web, sales, call-centers, social media, mobile data, Internet-Of-Things and so on) transforming them into solutions that guide companies to success.
We offer a variety of services, from Business Intelligence (BI) to Predictive Analytics and Big Data development, which influences the outcome of the business in real-time and helps extract more value from the data. Our expertise will give you the insight you need to predict new opportunities, capitalize on future trends, and respond to challenges before they happen.
We help businesses around the globe by developing efficient platforms for data storage and analysis. Those can then be integrated in a variety of ways with a range of heterogeneous sources. Depending on the need and the task, we can offer solutions based on Microsoft SQL Server, SQL Azure Data Warehousing, HDFS, Mapr NFS or HBase.
Being experts in Apache Hadoop and Apache Spark, we can offer the most rational approach to data processing and analysis. Our in-house specialists can launch a dedicated clusters, and tailor it to suit your exact business needs. We also use the most popular, and highly efficient PaaS offerings from top Big Data cloud vendors like Elastic MapReduce on Amazon AWS, Hadoop on Google Cloud Platform, HDInsights on Microsoft Azure, Spark on DataBricks Cloud. In some cases, depending on client’s needs, we can provide solutions, using such tools as Apache Hive, Impala, and others.
Our two-step approach in scaling Machine Learning algorithms involves modeling with R, Python (scikit-learn, theano, NLTK), Weka, Octave, and others, to prove that entire task can be solved efficiently using selected algorithms on the early stage. After testing and proving hypothesis we're moving to the next step - scaling our solution. Here we use tools like Spark MLlib, H2O, NVIDIA cuDNN, DeepDist or similar. Though, we don’t limit ourselves to existing frameworks. If there is a need, we can develop a custom solution from scratch using our solid expertise in Java, Scala or Python.
We cover the following types of machine learning tasks: classification, clustering, regression, anomaly detection, reinforcement learning and online learning, including cutting-edge approaches, such as Deep Learning, Deep Reinforcement Learning, Convolutional Neural Networks and Recurrent Neural Networks.
Mission-critical applications process large streams of data arriving in real-time. Our knowledge targets applications that run in distributed mode across tens to hundreds of machines, and tolerate a latency of several seconds. Using Spark Streaming or Apache Storm we can solve any kind of real-time analysis tasks, including online machine learning.
Data-driven methodologies and predictive analytics are one of the best ways to make conversion out of your target audience. Recommendation engines, the most popular data-enabled tool, have already helped dozens of businesses attract new clients and ensure meaningful engagement for the existing ones. Optimizing customer acquisition and loyalty campaigns are the guaranteed outcome upon your resort to data analytics as as service. Machine Learning and Natural Language Processing techniques make marketing and advertising smarter that is easier to adapt to the digital behaviour of your clients and capture such interactions that could never been traced before. Briefly speaking, Big Data Analytics approaches you to your clients, making them familiar, easily accessible, and ready for your offerings.
Data Analytics in a financial services domain include an advanced risk management with the focus on market liquidity risks, stress testing and capital adequacy. Fraud detection and management as in credit cards and payment transactions move to a new level. For a fraud detection and security that are key factors in such an industry, can be identified and prevented by machine learning techniques that will distinguish fraudulent activity from the normal user’s behaviour.
Development of new electronic medical devices and EHR apps have preceded mass application of Big Data for medical treatment purposes. Technical advance was one of the factors that greatly contributed to the development of the evidence-based healthcare - beginning of big data and analytics era. Nowadays the healthcare analytics went even further taking advantage of machine learning approaches. Going beyond clinical data collecting and having the most out of previously unused data sources will help in prediction of earlier unrecognized patterns. Perfectial finds predictive analytics approach to healthcare solutions extremely beneficial one, since products in this sphere will give advantages to all the components of the healthcare system: patients, hospitals, doctors, authorities and others.