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Data Engineering for AI

The data foundation that makes agents actually work.

Clean pipelines, vector stores, embeddings, lakehouses. The foundation agents need.

What we build

  • Data pipelines for AI workloads (batch + streaming)
  • Vector stores and embedding pipelines
  • Data lakehouse architecture (Snowflake, BigQuery, Databricks)
  • ETL/ELT for RAG grounding
  • Data quality, lineage, and observability

Tech stack

dbtAirflowSnowflakeBigQueryPineconeWeaviateKafka

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