We build on Claude and Databricks. These are the clouds, warehouses, pipelines and semantic layers we connect to and run in.
Azure
We position each tool within the rest of your data stack so ingestion, transformation, semantic modeling, BI and governance work together instead of becoming separate delivery silos.
We design governed AWS data platforms and ship machine learning and agentic systems into production. Our focus is the harder layers: SageMaker MLOps, Amazon Bedrock, and Bedrock AgentCore for agents that run in live operations.
Works with: SageMaker · Bedrock · AgentCore · Lake Formation · Redshift · Glue and EMR · Kinesis
Microsoft AzureWe modernize the Microsoft data estate and build enterprise AI where it is hardest: Azure AI Foundry and Azure OpenAI for production GenAI, Microsoft Purview for governance, and Synapse and Fabric for the analytics backbone.
Works with: Azure OpenAI · AI Foundry · Synapse · Data Factory · Microsoft Purview · Azure ML · Fabric
We design, implement and optimize Snowflake environments for scalable analytics, secure data sharing, Snowpark development and cost-disciplined operations. Warehouse tuning, resource management and governance controls improve performance while keeping spend predictable.
Works with: Virtual Warehouses · Snowpark · Secure Data Sharing · Dynamic Tables · Cortex AI · Resource Monitors
Microsoft FabricWe help organizations implement Microsoft Fabric to unify data engineering, lakehouse analytics, Power BI, governance and AI, with clear operating models. We have worked with Fabric since its early product cycle.
Works with: OneLake · Lakehouse · Data Factory · Power BI · Microsoft Purview · Copilot
We implement dbt Core and dbt Cloud to modernize transformation workflows, enforce data quality, and create an analytics engineering operating model your team can own. Implementation experience across Snowflake, Databricks, BigQuery and Redshift.
Works with: dbt Core · dbt Cloud · Staging and mart layers · Data tests · Incremental models · CI/CD
We implement Fivetran for reliable data ingestion across SaaS tools, operational systems and cloud databases. We design ELT pipelines with Fivetran at the ingestion layer and dbt at the transformation layer, with cleaner lineage and lower maintenance overhead.
Works with: Connector strategy · ELT architecture · Sync optimization · Schema evolution · Pipeline governance
CubeWe implement Cube as a trusted semantic layer between your warehouse and every downstream consumer: consistent metrics, enforced business logic, and SQL, REST and GraphQL APIs for BI, embedded analytics and AI. We integrate Cube with Snowflake, Databricks, BigQuery, Redshift, PostgreSQL and more.
Works with: Semantic modeling · Pre-aggregations · SQL, REST and GraphQL APIs · Multi-tenant analytics · Embedded dashboards
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We build on Claude and Databricks. When your data already lives in one of these tools, we work in it rather than move you off it.
We pick the right service per workload and tune for performance, resilience and cost.
Every engagement ships with security, CI/CD, observability and operational readiness, so systems scale after go-live.
Seven, grouped by the job they do: AWS and Microsoft Azure (clouds), Snowflake and Microsoft Fabric (warehouse and lakehouse), dbt and Fivetran (pipelines and modeling), and Cube (semantic layer). We build on Claude and Databricks; these are the clouds, warehouses, pipelines and semantic layers we connect to and run in.
No. Your stack stays yours: when your data already lives in one of these tools, we work in it rather than move you off it. We pick the right service per workload and tune for performance, resilience and cost, and every engagement ships with security, CI/CD, observability and operational readiness, so systems scale after go-live.
We design governed AWS data platforms and ship machine learning and agentic systems into production. Our focus is the harder layers: SageMaker MLOps, Amazon Bedrock, and Bedrock AgentCore for agents that run in live operations. We also work with Lake Formation, Redshift, Glue and EMR, and Kinesis.
On Azure we modernize the Microsoft data estate and build enterprise AI where it is hardest: Azure AI Foundry and Azure OpenAI for production GenAI, Microsoft Purview for governance, and Synapse and Fabric for the analytics backbone. On Fabric we unify data engineering, lakehouse analytics, Power BI, governance and AI, with clear operating models.
Yes. We design, implement and optimize Snowflake environments for scalable analytics, secure data sharing, Snowpark development and cost-disciplined operations. Warehouse tuning, resource management and governance controls improve performance while keeping spend predictable.
Yes. We design ELT pipelines with Fivetran at the ingestion layer and dbt (Core or Cloud) at the modeling layer, with data quality enforced and an analytics engineering operating model your team can own. We implement Cube as a trusted semantic layer: consistent metrics, enforced business logic, and SQL, REST and GraphQL APIs for BI, embedded analytics and AI.