Over the globe, around 90% of data is in unstructured format. With an exponential increase in this data, enterprises find it challenging to harness it for intelligent use. Here technology can be leveraged to provide sustainable business solutions and go beyond.

Artificial Intelligence (AI) / Machine Learning (ML) solutions are accomplished to process and correlate this unstructured data in any form to come up with intelligent patterns, data insights, and image identification. New age hybrid AI / ML engines join the dots across a plethora of multi-structured data assets to predict events and raise alerts.

NAlliance Tech AI is a comprehensive Artificial Intelligence and Cognitive Sciences platform that helps enterprises leverage use cases related to pattern detection, text & data mining, and computer vision. It helps enterprises extract intelligence from high volumes of high velocity data including structured, unstructured, and multi-structured data from diverse sources such as spreadsheets, documents, emails, PDFs, images, video files, audio files, social media, news, transactions, and other data types to discover actionable insights that are traceable.

This human data interface (HDI) uses various AI components including topic clustering, cognitive capture, sentiment capture, face detection, face match, face search, object classification, object detection, etc. It aggregates data and correlates seemingly unrelated data points to provide the bigger picture. It uses multiple data sources, data lakes, and databases to perform contextual analysis and sequence building.



AI Text

  • Document classification
  • Document clustering
  • Topic modelling
  • Accurate summarizations
  • Cognitive capture
  • Named Entity Recognition (NER)
  • Co-Reference in long texts
  • Underlying sentiment analysis in unstructured text
  • Emotion analysis
  • Word embedding for semantic and contextual searches

    AI Pattern

    • Pattern recognition
    • Association extraction
    • Co-relation of data points
    • Prediction of behavior and events
  • avatar-m

    AI Image

        • Face detection
        • Face match
        • Face search
        • Live face (selfie)
        • Object classification
        • Object detection


    Information Mining

    Artificial intelligence and machine learning methods for fast information mining with high precision as compared to key-word based or fuzzy searches Multi-lingual text mining and rule based mining


    Data aggregation and query platform to collect, validate, analyse data, and decide in near real-time

    Traceable Pattern Generation

    High-end technologies, such as natural language processing, advanced text analytics, advanced data analytics, stream analytics, for pattern generation with better accuracy and relevancy

    Alert Generation

    Alerts to highlight action items which need quick attention

    Classification And Grouping

    Text classification and clustering for seamless grouping of elements and records

    Document Highlights Generation

    Document summarisation for culling out key points before further processing as well as archival

    Data Extraction

    Information extraction for mining entity, event, topic models, key phrases, document scrutiny, etc.

    Pattern Mining

    Associations and relationships to uncover deepest patterns, rings, similar behaviours or implicit groups while projecting likelihood of events

    Data Indexing

    Real-time search and indexing engine for building a seamless archival-retrieval system

    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.

    Amit Mahajan
    N Alliance Tech Commercial Director

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