The reference for AI deployments that have to work.
Built for solo implementers at SMBs. No fluff. No vendor hype. Just what breaks, why it breaks, and how to fix it.
For Practitioners
- Stack & Tools — What to use, what to skip, what to watch
- Failure Modes — 8 ways AI deployments die, with recovery playbooks
- Deployment Patterns — Architecture by team size
- Operations & Maintenance — Post-launch reality
- Cost & Economics — Real TCO numbers
- Data & Knowledge — RAG, knowledge bases, data quality
- Compliance & Sovereignty — DPDP, GDPR, data residency
- Team & Adoption — Shadow AI governance, solo implementer patterns
For Leaders
Strategic briefs on AI deployment economics, team transformation, and competitive positioning.
Coming soon. One strategic brief monthly.
The Eight Failure Modes
- Adoption Stall — Built, launched, abandoned
- Silent Agent Failure — Wrong answers, zero errors
- Authentication Failure — Tokens expire at 2 AM
- Data Quality Failure — 40% stale data, 40% wrong answers
- Integration Failure — API drops, agent keeps running
- Knowledge Base Decay — Confident answers from stale facts
- Scope Creep — 250K Frankenstein
- Cost Overrun — The 18K
The Ten Core Concepts
- RAG — Retrieval that makes AI useful
- AI Agent — What it actually is
- Shadow AI — Governance for what you can’t see
- MCP — Protocol for tool connections
- Data Residency — Where data lives, why it matters
- LLM Drift — The model that changed without warning
- Self-Hosted AI — When to run your own
- TCO — The real cost numbers
- Vector Databases — Storage for retrieval
- Human-in-the-Loop — When to keep humans in the process
Editorial Standards
- Every claim carries a confidence label: [CONFIRMED], [OBSERVED], [UNCERTAIN]
- No vendor marketing. No AI-generated fluff. No “delve” or “harness.”
- Specific numbers, real examples, and the solo implementer angle.
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