NAlliancetech provides competitive edge to businesses by providing solutions across the Data Science and Data Analytics lifecycle helping them to analyse and take decisions about “what has happened (Descriptive), why has it happened (Diagnostic), what will happen in future (Classification & Predictive), and what action to take (Prescriptive)”. It provides one click solution for building Advanced Analytics models and visualization dashboards. Business users having no expertise in statistical analysis or programming languages, such as R and Python, can easily select variables and run these statistical models. Direct visualization of the results in desired formats makes it easy for the business users to understand the analysis and take appropriate action. It also provides automation of analysis dashboards and real-time dashboard and Advanced Analytics.

NAlliancetech uses its self-actuating 5D model that forms the crux of the Advanced Analytics, Data Sciences, and Data Mining paradigm. It enables businesses to Design, Develop & build, Deploy & implement, Discover & analyze the enterprise data, and Deliver value to mitigate risk and optimize existing resources. The company uses its flexible engagement models to suit the business requirements and its dedicated AI/ML innovation center to deliver cost efficient solutions.




  • Hypothesis testing
  • Chi-squared test
  • Z-tests and testing for variance



  • Recommendation
  • Decision
  • Action


Classification & Predictive

  • CHAID decision tree
  • Linear discriminant analysis
  • K-means clustering
  • Regression analysis
  • Linear, Logistic and Probit
  • Econometric time series modeling
  • Seasonal and non-seasonal forecasting

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  • Measures of central tendency
  • Measures of dispersion
  • Skewness and kurtosis
  • Raw and central moments
  • Correlation analysis


Deep Analytics

  • Data mining of complex and distributed architectures
  • Data analysis using algorithms on huge data volumes
  • Query-based search mechanisms for extracting best match

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Advanced Data Modelling

  • Entity clusters and Entity Relationship Modelling
  • Extended Entity Relationship Modelling
  • Dimensional modelling


Advanced Functionalities

  • User friendly data selection interface
  • Easy availability of data pre-processing
  • Improved rule for data validation
  • Navigate the user towards right solution with error generation
  • Outlier or possible fraud detection


  • Faster result generation through the progressive use of Artificial Intelligence / Machine Learning algorithms
  • Capability Augmentation
  • Multiple tab results available all together
  • Enhanced time series analysis
  • One click solution like Churn analytics, etc.


  • Interactive graphs along with comments
  • More visualization options available along with data grid
  • Enhanced time series graphs
  • Easily available customization
  • Predict future scenarios and prescribe solutions
  • Sentiment Analytics

Frequently Asked Questions

Artificial Intelligence solutions enable the automation of medium to complex processes and take Robotic Process Automation to the next level. It powers predictive maintenance of heavy machines thus reducing operational costs. AI enables pattern identification with vast data volumes thus augmenting human intelligence and decision making. It improves the analytics domain to the nth extent. While Natural Language Programing (NLP) algorithms read and augmedata nt to produce analytical reports, Natural Language Generation (NLG) algorithms write sentences.

AI joins the dots from disparate data sources and discovers interesting patterns that are less visible to the human eye and intellect. Consistent variations, clustering of data points, dependencies, etc., bring forth interesting stories from the stagnant data resources. Using data, AI-enabled information mining generates hypotheses, verifies them, and deduces information. Information Mining forms the basis of Predictive Analytics and Machine Learning.

AI-powered Data Mining enables users to perform different levels of tasks –

  • Anomaly detection, where unusual patterns or outliers are thrown up, which may be errors or require deeper investigation
  • Associations, where the relationship between two or more variables are brought forth, and is frequently used in forensic investigations
  • Clustering, where data points that are similar in more than one ways are discovered
  • Classification, where known structures are generalized in order to apply to new data points; for example: Records or File Classification
  • Regression, where a function is found, which models the data and estimates the relationship between different data points
  • Summarization, where a concise summary is generated based on weighted keywords. It is popularly used for generating audit reports and executive summaries.

AI simplifies Document Management to a great extent. It enables automatic file classification or categorization as well as summarization. The files may include spreadsheets, documents, emails, PDFs, video files, audio files, social media, news, and other data types.

AI algorithms generate intelligence from high data volumes by correlating disparate data points. This data may comprise structured, unstructured, and multi-structured data.

A recent feature is AI-enabled Topic Modelling. It allows screening of huge data sets, such as reviews, emails, social media snippets, etc., and segregating them as per the predominant sentiment.

AI algorithms use weighted keywords and correlate them to generate concise summaries of lengthy documents, news articles, research papers, agreements, books, tweets, etc.

It uses two methods – Extractive and Abstractive. Extractive Summarization extracts several portions of the text and stacks them to create a summary. Abstractive Summarization uses NLP algorithms and generates a new summary.

AI-powered document summarization is used in audits, research study scenarios, social media listening, government services, etc.



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Amit Mahajan
N Alliance Tech Commercial Director

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