Data Architecture for AI
Latest articles on Data Architecture for AI from Cortado Group
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Your AI is Blind, and Your "Clever" Naming Strategy is the Culprit
By David Russell
The first step in any AI implementation is data. This is the core truth **we** bring to every client engagement. AI inherently needs data to succeed-it can't forecast *your* close rates until it knows *your* current close rate. It can't tell if 90-day-old deals are dead if *you're* not accurately tracking open and close dates. It needs this fuel to do its magic.
Your data architecture is the blueprint for your AI's success. But what happens when that blueprint is riddled with cryptic codenames? Explore why robust data architecture, specifically designed for findability and semantic clarity, is crucial to prevent 'machine blindness' and unlock the true power of AI.
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Conversations to Conversations 4: Integrating AI Systems and Human Expertise
By David Russell
This week, our work on our AI focused on a dual objective: advancing the core architecture of our AI-driven meeting intelligence platform while simultaneously…
Stop thinking about data architecture as just storage. We're building a semantic "memory layer" for our entire platform. This post details our move from ad-hoc retrieval to a persistent RAG index.
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AI is a Chainsaw. Are You Using It Without a Guard?
By David Russell
AI can be a powerful tool, but without proper safeguards, its misuse can lead to costly mistakes and missed opportunities.
Reliable AI begins with disciplined data architecture. From automated validation to source grounding, this piece shows how to build the technical foundations that make AI outputs explainable, traceable, and trustworthy.
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SKU 101 Foundations
By David Russell
Solid SKUs are essential for effective Go-to-Market AI, as they ensure accurate insights and boost ROI by preventing costly data errors.
A strong SKU system is data architecture in action. Learn how logical, consistent identifiers create the structure AI needs to deliver accurate outcomes.