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Traditional ITIL support and infrastructure services do not fit the bill for enterprise analytics solutions that operate on modern data platforms. We help you prepare for the production use of AI/ML and analytics solutions and maximize their potential through an analytics-first service model. We integrate our services with enterprise support solutions like ServiceNow, for a seamless support experience for business users.
- Analytics applications maintenance services
- Flexible capacity models for support operations with global coverage
- Reduce your costs and greater ROI on analytics solutions
With new digital technologies and the technology bar-raising all the time, enterprises need fluid analytics applications and unleash the power of BI solutions like PowerBI and Tableau. Traditional ways of developing applications are passe; enterprises need agile, cross-platform, and cloud-native analytics and AI/ML applications that bring the power of data and insights to your consumers. We make sure enterprises get the best-in-class design for user experience, the interactive dashboards to support present and future business requirements using scalable infrastructure and engineering tools.
- Simplify, transform and drive BI solutions usage,
- Enable faster insight-to-decision cycles,
- Transform reporting solutions with enhanced capabilities like real-time, geo-visualizations and time-series,
- Best-in-class web applications for data-driven insights.
Unlock the power of data and focus on driving smarter, data-driven decisions through data lakes and platforms. It is today the fastest way to get answers for all your data to internal consumers. Free your data from legacy data source systems, organization silos, and enrich the data to derive insights for competitive advantage. Well-designed and built, scalable, and cost-effective, enterprise data lakes are the next generation for data management solutions. A combination of internal, external, structured, semi-structured, and unstructured data, combined with Cloud and the power of cognitive solutions, can deliver insights that transform decision making.
- Modernize enterprise data landscape to unleash analytics capabilities,
- Streamline enterprise data assets and reporting solutions,
- Optimize your ETL to reduce time-to-insights,
- Enable self-service based analytics using Data Lakes and Data Hubs.
Harness the power of digital data to understand customer behavior and improve digital experiences. Tailor-made experiences across channels drive the performance of digital investments and specialized datasets in e-commerce, digital marketing, and ad campaigns. We deliver digital strategy consulting and implementation solutions that understand the influence and returns for each digital analytics decision.
- Advanced analytics for personalization and real-time targeting
- Optimize your investments in ad platform exchanges
- Audience analysis and segmentation
- Digital strategy and governance
- Map your customers’ digital journey
At the intersection of data science and software engineering, ensure the success of your AI/ML solutions by bringing specialist skillsets for IT / DevOps, software engineering, and AI/ML. Data scientists are released from the other tasks required to bring AI/ML solutions to life with the combined methodology of AI engineering and machine learning to provide actionable insights. We can help businesses apply AI, engineering, and MLOps to derive meaningful value from their AI/ML investments. We work towards:
- Rapid innovation through automated ML lifecycles,
- Well documented and reproducible AI/ML flows and models,
- Modularized stages in the AI/ML pipelines from data wrangling to production deployments,
- AI/ML governance and monitoring,
- Easy deployment of AI/ML models across deployment targets.
With most analytics initiatives starting as cloud-native solutions and also need to leverage advanced PaaS services, BI solutions available on the Cloud, Cloud engineering needs to build and maintain data lakes, operate data pipelines, and enable value realization of Cloud investments. This Cloud-driven analytics has become indispensable for any enterprise wishing to have agile analytics and be driven by data and insight-rich decision making.
- Leverage the power of cloud data warehouses and Data Lakes,
- Build analytics solutions with cloud-first architectures,
- Modernize applications and data platforms,
- Transform analytics to Cloud-based environments,
- Leverage data engineering with public Cloud capabilities (DevOps, CI/CD).
Start your end-to-end cloud journey right from on-premise systems to the Cloud, from Cloud consulting, architecting, designing, migration to implementation, and ITIL-based operations, leading to an analytics-driven enterprise-ready for growth.
- AWS, Azure and GCP engineering for end to end applications
- Spark/ Scala analytics workloads
- Microservice architectures
- IoT/stream analytics
- Scalable architectures using DevOps and MLOps
- Model registry and ML CI/CD pipelines on Cloud
- AI/ML platforms for Data Science
- Web analytics
- Digital marketing analytics
- Ad platform technologies
- Dimensional modeling
- Data warehouse and Data marts design
- Data and platform governance
- Database migration to the cloud
- ITIL/ITSM services for data platforms
- Interactive dashboards
- Automated reporting solutions
- Complete web & mobile applications development
- App modernization & migration to Cloud
- ITIL based Support Desk for analytics infrastructure management and monitoring
The making of well-designed AI / ML solutions requires significant data engineering and data wrangling exercises. Data engineering and scalable modern solution architectures are key requirements for an AI/ML solution for production use. We use a business-focused approach to IT in engineering the solution, aligning analytics, AI/ML approaches, and technology. Unleashing agile analytics within an enterprise where data is imprisoned in legacy platforms and infrastructure requires not just an IT transformation – but a data-first approach driven by an analytics partner. Finally, to understand how the data-to-decision making comes together requires excellent team dynamics and analyzing, designing, and building the AI/ML application.