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GITL's digital solution offerings are spread across strategic advisory and consulting, recommendation of solution frameworks and providing implementation services.

Digital Transformation Consulting Services:

  • Business Transformation Consulting
  • Enterprise Data Management Assessment Studies
  • Big Data Strategy and Roadmap

Advanced Analytics:

  • Descriptive, Predictive, Prescriptive, Cognitive Analytics
  • Streaming Analytics (IoT)
  • Artificial Intelligence
  • Digital Dashboards
  • Robotic Process Automation

Digital Asset Management Services:

  • Data Platform
  • Big Data Services

Data Ingestion

  • Transactional Data: Stream Sets
  • Log Data: Flume, logstash, Log4J, fluentd
  • Streaming Data: Spark Streaming, Storm, Apache Kafka, Amazon Kinesis Streams

Data Distillation

1. Batch

  • Batch: Map Reduce
  • Script: Pig
  • SQL: Hive
  • Data Frame: Spark

2. Interactive

  • Cache: Amazon Elastic Cache
  • NoSQL: Amazon DynamoDB, MongoDB, Cassandra
  • SQL: Amazon RDS
  • Search: Amazon ES, Apache Solr

3. Real - Time

  • Amazon Kinesis Firehose
  • Storm
  • Spark Streaming
  • AWS Lambda

Model Building

1. Open Source

  • Python
  • Spark MLlib
  • H2O

2. Commercial

  • Amazon ML
  • Azure Machine Learning
  • SAS
  • SPSS


  • Tableau
  • Qlik View
  • Spotfire
  • MicroStrategy
  • PowerBI
  • 1Service Offering
  • 2Industry Solutions
  • 3Benefits

Service Offering

  • Product management

    Advanced Analytics including Real Time Dashboard and BI.

    Descriptive, Predictive, Prescriptive, Cognitive Analytics, Streaming Analytics.

  • Replenishment

    IoT integration

    Sensor and sensor enabled hardware devises will generate real time data for actionable insights, which can be streamed into the mobile application through IoT Cloud. GITL has deep and diverse expertise in designing and configuring innovative IoT solutions that can be integrated with Gateways, IoT Clouds and other platforms to ingest real live stream of data to generate Real Time Analytics reports.

  • Powerful pos

    AI, Machine Learning and Deep Learning

    AI and Machine Learning algorithms are used in various functional areas including Manufacturing, Retail, Human Resource Management, Aviation, Healthcare and the likes for predictive analytics, pattern recognition and Prescriptive analytics.

  • Product management

    Domain specific Digital Transformation solutions

    SCM: Optimize your inventory, Demand Analysis and Forecasting, Optimize MRP & Route, Machine and Asset Utilization using Predictive Machine Maintenance Analytics, Retail: Capture & Analyze Footfall data, Predict Footfall, Improve Average Service Time at POS, Capture Average Dwell Time across aisles, Improve Product Assortment using Market Basket Analysis, Reduce Perishable Wastes in Retail using IoT and Real Time Dashboard.

Industry Solutions

  • Brochure


    • Smart Factory Solutions using Industry 4.0 guidelines and digital technologies like Big Data, IoT, Cloud Computing and Robotics
    • Product portfolio optimization based on real-time data and insights
    • Production impurity prediction using advanced pattern recognition techniques
  • Case Studies


    • Precision marketing to identify customer retention, cross-selling and up-selling opportunities
    • Cohort analysis to determine how customer behavior changes over time
    • Chatbots for conducting real-time conversations with customers

  • Case Studies

    Supply Chain

    • Transport cost optimization using mathematical models and linear programming

    • Liquidity forecasting and cash-flow projections

    • End-to-end supply chain visibility, responsiveness, and resilience


  • Brochure

    Real Time Machine Monitoring

    • Sensors for noise, humidity, temperature, and acceleration are used to generate warnings and alerts in real-time. Furthermore, anomaly detection is done using machine learning algorithms. Organizations can benefit from machine failure prediction strategies and can make decisions based on the remaining life of their assets.
  • Case Studies

    Analysis Of Unstructured Data

    • Digital technologies can be used to pre-process and transform textual unstructured data into structured data. Various text analysis such as sentiment scoring, emotion tagging, topic modelling, tokenization and trend finding are done to identify issues and find relevant solutions to business scenarios.

  • Brochure

    Sentiment Tapping

    • Organizations can benefit from real time sentiment analysis dashboards which show brand perception, trends, purchase conversion rates and other business KPI. Using these solutions, social data is integrated with analytics techniques.
  • Case Studies

    Inventory Optimization

    • By identifying the exact factors which lead to inventory pileup, organizations can improve performance by lowering the inventory to sales ratio. Sensitivity scenario analysis enables decision makers to understand the impact of endogenous factors on inventory. This results in reduction of working capital and better order fulfilment metrics.
  • Case Studies

    Sales Recommendations

    • Offer Superior Customer Experiences Faster Response Time Sales Recommendations Digital solutions help organizations to take a concentrated sales approach based on a pre-determined Must Sell List (MSL). The list is prepared by creating association rules based on patterns in monthly sales transactions and by identifying the key factors influencing the sale of SKUs at a granular (sub-channel) and (outlet) level.