Your AI isn't broken, your data context is; you need solid data engineering to bridge the gap between a smart model and a reliable, real-world business agent. Most enterprise AI investments today ...

Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving ...

Every organization is facing the same problem: engineering teams don’t lack data. They lack context for that data.

Every few months, the enterprise AI conversation resets around the same flawed premise that better models solve the problem. When large language models hallucinate, the instinct is to reach for a ...

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