Blog
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From Context Engineering to Decision Engineering
Sit in on almost any enterprise AI discussion today and within minutes someone says “context.” Rightly so. I have long argued that many agentic…
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Your Enterprise is Overpaying for AI and Not Because You’re Overusing It
Most companies send every AI request, from “find me the leave policy” to “give me the reason for the last three quarters’ sales decline,”…
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Near-Real‑Time Credit Card Fraud Detection Solution Framework built on Databricks Lakebase and Genie
Executive Summary Financial institutions today face mounting pressure to detect and prevent fraudulent card transactions at the very moment they occur, rather than relying…
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A Solution Framework for Real-Time Claims Processing, built on Databricks Lakebase
Why the Industry Needs Real-Time Claims Processing Insurance claims processing is a critical function for insurers, covering the full lifecycle from claim submission and…
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From Oracle Complexity to Databricks Velocity: Modernizing Mission-Critical Enterprise Workloads in the Agentic AI Era
For decades, Oracle has been at the helm of some of the most critical data workloads in the enterprise: Exadata estates, PL/SQL packages, ODI…
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The Context Gap: Why Your Agentic AI Investment Isn’t Paying Off (Yet)
Enterprises are pouring significant investments into agentic AI, yet many aren't seeing the returns they expected. The underlying issue isn't the technology — it’s the ‘context gap’: AI models lack the enterprise-specific…
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Agentic AI Won’t Scale Without Enterprise Context
Many enterprises are focusing on AI models, infrastructure, and prompt engineering while ignoring the real bottleneck—enterprise context. As a result, most agentic AI initiatives remain stuck…
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