Insights From
Practitioners
Hard-won lessons from building 50+ production AI systems. Strategy, engineering, and deployment — from the teams who do the work.
The Rise of Agentic AI: Beyond Chatbots to Autonomous Systems
Why 2026 is the year agentic AI moves from research labs to production systems — and what enterprises need to know to get ahead of the curve.
Read Article →RAG vs Fine-tuning: Choosing the Right Approach for Enterprise LLMs
A decision framework for when to use retrieval-augmented generation versus fine-tuning your own models.
Why 87% of AI Projects Fail — And How to Be in the 13%
The most common failure modes in enterprise AI projects and the engineering practices that prevent them.
Building Production-Ready GenAI: Lessons from 50+ Deployments
Hard-won lessons on prompt engineering, evaluation, guardrails, and observability from real production systems.
AI Governance in 2026: What Every Enterprise Needs to Know
Navigating the evolving regulatory landscape for AI — from the EU AI Act to industry-specific compliance requirements.
From Pilot to Production: The MLOps Bridge Most Teams Miss
The infrastructure gap between a working notebook and a production ML system — and how to close it systematically.
How Startups Can Leverage AI Without a Data Science Team
Practical strategies for resource-constrained startups to build AI capabilities using modern tools and frameworks.