End-to-end data engineering, MLOps, and analytics — production-ready architectures, repeatable deployments, and measurable outcomes.
From strategy to production: we cover data platform design, pipeline engineering, ML deployment, and analytics productization.
Design resilient cloud-native data platforms, define schemas and lineage, and set up secure access and observability.
Implement robust, tested pipelines with monitoring, retries, and schema checks for reliable downstream analytics.
Productionize models with reproducible pipelines, monitoring, A/B rollout, and drift detection to keep predictions reliable.
Prebuilt patterns and bespoke implementations for product analytics, real-time metrics, personalization, and automated reporting.
Event tracking, sessionization, and a metrics layer that maps to business KPIs — delivered with dashboards and a metrics contract.
Low-latency feature stores and inference endpoints powering personalization and prioritization across web and mobile products.
A pragmatic, outcome-driven process: align, build, ship, and measure.
Define goals, users, and success metrics.
Architect schemas, infra, and data contracts.
Ship pipelines, tests, and CI/CD for data.
Monitoring, SLAs, and iterative improvements.
Selected projects demonstrating impact on revenue, retention, and efficiency.
Consolidated event streams and canonical metrics reduced reporting time by 70% and improved feature prioritization.
Built a low-latency feature pipeline and serving layer, increasing CTR by 18% in A/B tests.
Automated ETL and anomaly detection saved engineering time and caught billing issues early.
Schedule a free 30-minute discovery call. We'll assess your stack, priorities, and roadmap for high-impact wins.