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Platform Engineering for AI Teams: The Missing Layer That Makes AI Actually Work in Your Org

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The Problem Every Growing AI Organisation Hits

Picture this. Your organisation decides to invest in AI. Three teams get the green light to build AI-powered features simultaneously.

Team A (customer service) spends two weeks building a Lambda function that calls Bedrock, adds basic error handling, and connects it to a Slack bot.

Team B (internal tools) spends two weeks building a Lambda function that calls Bedrock, adds basic error handling, and connects it to a web interface.

Team C (product recommendations) spends two weeks building a Lambda function that calls Bedrock, adds basic error handling, and connects it to their recommendation engine.

Three teams. Six weeks of combined engineering time. Three nearly identical implementations that differ in small, inconsistent ways. No shared prompt management. No shared cost tracking. No shared evaluation framework. No shared security controls. And when the company decides to switch from Claude 3 Haiku to Claude 3.5...

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