Hard-won lessons from shipping AI to production, building data platforms that hold up under audit, and modernizing .NET stacks without multi-year rewrites. Written by the engineers who do the work — not a marketing team.
Most AI pilots die between demo and production. The causes are predictable: no eval suite, no monitoring, no data-quality baseline, and no owner with P&L accountability. We break down what the teams that ship have in common.
Tool-calling agents can create CRM records, move money, and send emails. Without allowlists, schema validation, and confidence-gated escalation, one bad inference becomes a production incident. We detail the six controls that make agentic AI auditable.
Both are capable platforms. The right choice depends on your existing Microsoft licensing, team SQL proficiency, and whether Power BI is a first-class requirement. A decision framework with real cost comparisons.
Shipping an AI feature without an eval suite is like deploying code without tests. One client lost $180K in a week from a silent accuracy regression. We show how to build eval suites that scale and what they cost to maintain.
You can't add AI features to a monolith that deploys monthly and has zero observability. We show how containerization, CI/CD, and tracing pay for themselves in reliability gains within the first quarter — and unblock the AI roadmap as a side effect.
It's not a data lake. AI-ready data means governed schemas, freshness SLAs, access controls that work for both humans and machines, and a semantic layer that eliminates the "my numbers don't match" meeting. We define the standard and show what it costs to get there.
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