Datarmatics AI and Digital Services
For a decade, the data platform debate was “which warehouse?” Snowflake, BigQuery, Redshift — pick one, load everything, pay per query. In 2026, the debate has shifted. The question is no longer which proprietary warehouse owns your data, but whether you need a warehouse at all — or whether an Apach…
The model was not the problem. Neither was the prompt. The RAG pipeline retrieved the right documents, generated fluent prose, and confidently stated that the enterprise refund window is 60 days — when the actual policy says 30. The source document was a deprecated Confluence page from 2022 that nob…
Your RAG pipeline answers simple lookup questions flawlessly. “What is our refund policy for international orders?” — perfect citation, accurate answer. Then a VP asks: “How do our supply chain disruptions in Q2 connect to the customer churn spike in the enterprise segment?” The vector search return…
The demo worked perfectly. Your RAG chatbot answered every test question correctly in the notebook. You shipped it on Friday. By Monday, users were reporting wrong refund policies, fabricated statistics, and SQL queries that returned plausible but incorrect revenue numbers. The model did not degrade…
Every enterprise AI project eventually hits the same wall: the model is capable, but it cannot reach the systems where your data lives. Custom integrations multiply — one connector for Snowflake, another for Jira, another for your internal API. By the time you have five tools wired up, you are maint…
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