Gartner estimates that by 2021, 40% of new enterprise applications implemented by service providers will include AI technologies.

Artificial Intelligence and Machine Learning have come a long way since the days of Turing Test and Frank Rosenblatt’s first neural network in 1957. Increased data processing power, the rise of Big Data Analytics and ability to store and analyze huge volumes of data and improvement in algorithms are significant contributors to this shift of momentum. Leading global enterprises like Amazon, Google and Microsoft are investing heavily in Machine Learning Platforms to explore the hidden opportunities within data lakes across enterprises.

At CIGNEX Datamatics, our experts adept at leading artificial intelligence and machine learning frameworks would help you solve real-life business challenges and develop your unique concepts from idea to production.  Here is a brief overview of a selected set of tools that we use.

  • Amazon Sagemaker – Fully managed service that makes it easier for enterprises to build, train and deploy machine learning models at scale.
  • Caffe2 – Lightweight, modular and scalable Deep Learning Framework that can be easily built for enterprise scale with their massive cross-platform libraries
  • TensorFlow – Leading Open Source machine learning framework that allows numerical computation


We have created end to end (document acquisition to classification) solutions leveraging machine learning tools to create a model that automatically classifies documents based on categories defined by the organization

Document Clustering

Using descriptors to create a cluster of documents. Leveraging cluster analysis to group similar documents together for further intensive analysis

Predictive Analytics

Leveraging Artificial Intelligence and Machine Learning to create fully automated predictive analytics enabling enterprises to predict business & industry trends, understand customers, improve business performance, identify risks and drive decision making at much higher levels of efficiencies & costs.


Once we understand the use case and the nature of data we develop an approach document where we highlight the five necessary stages of machine learning.

  • Data Acquisition – Here we collect data from internal and external (publicly available data, streaming data, logs) sources using ETL platforms
  • Data Preparation – We clean, tokenize, rectify and reformat the data to make it appropriate to the Business Use Case
  • Hypothesis, Data Modeling, and Algorithm Evaluation – Applying various algorithm and then compare the observations and results and selecting the algorithm that delivers the optimum result
  • Model Fine Tuning  Improving the Data Model based on the observations to further improve results
  • Data Presentation  Delivering the final analysis in the form of reports, dashboards, portal platforms etc. that would simplify decision making

Over a period of time, our approach is to automate each phases to minimize resource footprint and improve efficiency across the data lifecycle.

Featured Case Study

Achieving High Accuracy in Reduced Time with Machine Learning

The manual segregation of documents was replaced with an intelligent automated classification model. The solution includes a parser (Apache Tika + custom)  for content analysis and detection, a classifier  (D4LJ, Naïve Baiyes) to create model and run test set, a reviewer to audit logs, docs parsed and outliers, and review unclassified documents. The solution achieved high accuracy (>95%) while maintaining the high performance all completed in shorter duration to the traditional classification process.

Read Case Study

Why CIGNEX Datamatics?

Our work on Big Data Analytics and AI/ML is flexible as we deploy solutions on-premise and as managed services. Over the years, we have built solutions using a technology mix which includes Data Integration, Data Management, Data Visualization and Advanced Analytics.