Take the data journey - Move from Data to Information to Knowledge and finally to Wisdom. Data is a powerful asset and should be treated as Product. Successful organizations are becoming "Data Driven Organizations" as they start their journey of AI. Build a strong enterprise data strategy. Utilize modern data architectues like data mesh, data fabric and data lakehouse. Secure your data, govern you data. Think about data quallity and lineage.
Partner with business and technology leaders to develop a pragmatic data strategy that aligns with corporate goals. We define target architectures, roadmaps, use-case prioritization, KPIs and a data product operating model to unlock measurable business outcomes.
Evaluate your current data estate, people, processes and tools to identify gaps and quick wins. Our assessment produces an actionable remediation plan covering data quality, lineage, security, storage and engineering effort estimations for prioritized initiatives.
Design and implement resilient lakehouse, mesh or fabric-based architectures that scale for analytics and AI. We select the right architecture patterns, cloud services and integration approaches to balance performance, cost and operational simplicity.
Build robust, production-grade pipelines for batch and streaming workloads using modern ETL/ELT, orchestration, and transformation tooling. Our teams implement scalable ingestion, transformation, cataloging and delivery patterns with observability built-in.
Implement lifecycle, cataloging, metadata management and storage strategies to make data discoverable and reusable. We provide data catalog, retention and storage optimization approaches to reduce costs and accelerate analytics and ML adoption.
Establish guardrails for data access, stewardship, policy enforcement and regulatory compliance. Our governance frameworks balance control with agility so teams can innovate while ensuring privacy, security and auditability.
Define measurement metrics and automated checks to improve trust in analytical and ML datasets. We implement monitoring, remediation workflows and lineage tracing so stakeholders can quickly understand data origins and confidence levels.
Turn insights into action with interactive dashboards and self-serve analytics built around your users. We design performant, role-based visualizations and reporting layers that surface high-value metrics and enable data-driven decisions.
Introduce DevOps principles into your data lifecycle — CI/CD for pipelines, automated testing, and end-to-end observability. This reduces incidents, accelerates delivery and keeps production data reliable for analytics and ML models.
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Design and implement scalable data warehousing solutions using Amazon RedShift for high-performance analytics, along with AWS Data Lake architectures on S3. Our expertise includes data lakehouse implementations, RedShift Spectrum for querying data lakes, and building end-to-end data pipelines using AWS Glue, Lambda, and EMR for enterprise-scale data analytics.
Implement comprehensive data solutions using Azure SQL Database, Azure Cosmos DB for globally distributed NoSQL databases, and Microsoft Fabric for unified analytics. We specialize in building modern data architectures, real-time analytics, data integration, and AI-powered insights using the complete Azure data ecosystem including Synapse Analytics and Power BI integration.
Leverage Google Vertex AI and BigQuery for advanced data analytics, machine learning, and AI-powered insights. Our experience includes building data pipelines, implementing MLOps workflows, training custom ML models, and deploying production-ready AI solutions using Google Cloud's unified data and AI platform for enterprise analytics and predictive capabilities.
Design and implement cloud-native data platforms using Snowflake's data cloud for seamless data sharing, warehousing, and analytics. Our expertise spans multi-cloud Snowflake deployments, data sharing across organizations, performance optimization, cost management, and integration with modern data tools for unified analytics across structured and semi-structured data.
Build lakehouse architectures using Databricks for unified data engineering, data science, and analytics. We specialize in Delta Lake implementations, Spark-based data processing, MLflow for MLOps, collaborative notebooks, and creating scalable data pipelines that combine the best of data lakes and data warehouses for modern analytics and AI workloads.
Design and implement optimal database solutions using both relational databases (PostgreSQL, MySQL, SQL Server, Oracle) and NoSQL databases (MongoDB, Cassandra, DynamoDB, Couchbase) based on specific use cases. Our expertise includes database migration, performance tuning, high availability configurations, and hybrid architectures that leverage the strengths of both paradigms.
Client: UK-based Automobile and Home Insurer. 500,000 quotes a day, 4 million customers
Scope: Migration of Guidewire, Apps (100+), APIs, Microservices, Data, Infra and DevOps to Azure.
Europe's first successful modernization and migration of Guidewire to cloud (> 15 mil GBP)
Tech stack: Azure, Azure VMware Solution (AVS), APIM, Snowflake, Azure Data & Integration, Guidewire
Outcome: Increased performance by 1.6 times. 4 to 5 times faster insurance adjustments. Legacy-free IT estate, APIfication, automation, data modernization, and operational cost savings.
Client: Asia based health insurance and financial services major
Scope: Modernization and cloud migration of legacy and complex apps to Azure.
Apps: 20+
Lines of code: 2 million +
Technology: Azure, Java, .NET, Sybase, SQL Server, DB2, WebSphere MQ
Client: Middle east based petroleum and natural gas major
Scope: Define strategy & platform architecture for a new Joint venture organization that would be curved out from parent company
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Placeholder text describing Agentic AI capabilities and solutions. This section showcases our expertise in autonomous AI agents and intelligent automation systems.
