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Analytics engineering for modern data teams
dbt is a data transformation framework that enables data teams to transform data in their warehouses using SQL and software engineering best practices. Their biggest announcement is the dbt Fusion engine, a complete rewrite of dbt from the ground up which is built on rust which makes it 30x faster than dbt core.
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dbt is positioning itself as the "data control plane" for enterprise analytics and AI. We have been using dbt in almost all of our projects. It has become a defacto modeling layer for us. Everyting from Macro's to test cases add modularity when building the analytics layer.
Partnership Focus Areas
Building reliable, modular, and scalable analytics pipelines
Version-Controlled Transformations Structured, testable data workflows
Collaborative Development Shared standards across analytics teams
Automated Testing Confidence in data quality and consistency
Analytics at Scale Faster iteration from raw data to insight
Absolutely. With experience across regions and industries, our team has delivered solutions spanning supply chain, marketing, finance, and operations - creating unified, decision-ready intelligence across the enterprise.
Most clients begin to see impact within 90 days - from predictive demand forecasting to AI-enabled pricing and retention optimisation. We focus on quick wins that scale sustainably.
Yes. Our engineering approach ensures seamless integration with platforms like SAP, NetSuite, Snowflake, and BigQuery, making AI and analytics adoption frictionless across your tech stack.
We don’t just deliver dashboards or models - we build AI-ready ecosystems that connect data, business, and technology. Our focus is decision adoption, not just data availability.
We start with the key business decisions that matter most in your sector - then design data foundations, models, and AI systems that improve them measurably. Each industry playbook is based on real-world use cases and continuously refined through deployment experience.
Case studies
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