ArchiDecode

The shift from “LLM integration” to an enterprise AI platform A request to “integrate an LLM” is rarely a single integration. As soon as an enterprise runs more than one model (or the same model in multiple regions), the hard parts move from application code into architecture : throughput, token budgets, quotas, availability, policy enforcement, observability, and cost attribution. You can see why in provider documentation: OpenAI measures limits across multiple dimensions (

Why point-to-point integrations and batch medallion pipelines don’t scale in regulated environments Executive Summary Enterprises repeatedly face a simple requirement: System A needs data from System B .But at scale, this becomes a structural problem leading to duplicated transformations, tight coupling, inconsistent business definitions, and governance gaps. Traditional approaches usually fall into two extremes: Point-to-point integration (fast but fragile) Medallion-based

A Practical Architecture Playbook to Reduce Duplication, Cost, and Risk Banks repeatedly solve the same problems across domains Credit Risk, AML, KYC, Operations, Finance, and IT often with separate teams, duplicated platforms, and inconsistent governance . As architects, this is a systemic inefficiency , not a delivery issue. This post outlines proven multi-tenant AI platform use cases that can be built once , consumed bank-wide , and governed centrally while still allowin

Where I’m Starting Right now, I’m deep into an AI project that’s both exciting and challenging. The mission? To design and implement an...

In the era of AI-powered automation, compliance and risk systems must adapt to frequent policy changes without downtime or risky model...