Service domain
From raw data to competitive intelligence — end to end.
Build a modern, scalable data foundation with cloud-native architecture, AI/ML solutions, and enterprise data governance. From strategy to production, we deliver the intelligence infrastructure that powers competitive advantage.
Data capabilities
Architecture, governance, engineering and AI are designed as one operating system—not isolated workstreams.
Self-service analytics, automated pipelines, governed BI environments
We design and implement governed self-service analytics environments that empower business users while maintaining data integrity. Our DataOps practice automates pipeline orchestration, CI/CD for data, and monitors data freshness end-to-end. From semantic layers and certified data marts to Tableau and Cognos dashboards — we build analytics experiences that business users actually adopt.
Policies, stewardship, lineage, and observability
We implement enterprise data governance frameworks covering metadata management, data lineage, master data management (MDM), and automated data quality monitoring. AI-powered validation rules and continuous quality scoring ensure your data is reliable at every point of consumption. We establish ownership, stewardship models, and audit trails that satisfy both regulators and business stakeholders.
ML model development, LLM integration, AI use-case discovery
We help organizations move from AI experimentation to production AI deployment. Our team designs ML pipelines, builds predictive and prescriptive models, and integrates AI outputs into business workflows. We also scope and deliver Generative AI use cases — from internal knowledge assistants to LLM-powered document processing — grounded in your enterprise data.
ETL/ELT pipelines, lakehouse architecture, third-party data integration
We build and modernize data engineering foundations — from high-volume ETL/ELT pipelines to lakehouse architectures on Databricks and Snowflake. We integrate third-party data sources (market data, geospatial, syndicated) and build enrichment layers that give your models and dashboards the context they need to deliver real insight.
AWS, Azure, GCP migrations; DataOps; infrastructure modernization
From legacy on-premise data warehouses to modern cloud-native platforms, we manage the full migration lifecycle. We assess your current estate, design the target architecture, execute migration with zero data loss, and optimize post-migration performance and cost. Deep expertise spans IBM Cloud Pak for Data, AWS, Azure, and GCP — including full DataOps practice build-out.
Delivery model
A proven engagement model from data audit to scaled production.
Assess current data estate, define business value targets
Architect the target platform, governance model, and roadmap
Deliver pipelines, models, and dashboards in sprints
Monitor, iterate, and scale with ongoing DataOps support
Built a real-time data platform tracking 8,000+ SKUs across 34 countries to optimize distributor inventory and secondary sales forecasting.
Key Result
34 countries, 8K+ products tracked in real time
Implemented an end-to-end AI process control solution on factory floors to reduce energy consumption and predict equipment failures.
Key Result
$39M cost savings, 2% CO₂ reduction
Modernized legacy on-premise data warehouses into a unified cloud data platform, centralizing commercial and clinical data for 5 business units.
Key Result
Unified data platform across 5 business units
Designed a centralized enterprise reporting platform, automating 200+ manual reports and empowering executives with self-service analytics.
Key Result
Reporting cycle cut from days to hours
Deployed a centralized Machine Learning Operations (MLOps) platform, accelerating model development from concept to production for credit risk and churn.
Key Result
20+ ML models in production within 6 months
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