Blog
AI strategy, implementation, and the evolving landscape — written for builders and decision-makers.
How to Build a Production-Ready RAG Pipeline in 2025
Retrieval-Augmented Generation has moved from research curiosity to the default architecture for knowledge-intensive AI applications. Here's what it actually takes to ship one that works in production.
Cursor vs Copilot: Choosing the Right AI IDE for Your Engineering Team
Both Cursor and GitHub Copilot promise to make your engineers faster — but they make very different bets on how. This breakdown covers fit, adoption patterns, and what forward deployment looks like for each.
Glean vs Custom RAG: When to Deploy an AI Product vs Build Your Own
The question isn't always "which RAG pipeline should we build." Sometimes the right answer is deploying Glean and getting to value in weeks instead of months. Here's how to decide.
Calculating Real AI ROI: What the Vendors Won't Tell You
AI vendors love to cite productivity statistics. Here's how to build your own ROI model — accounting for implementation costs, maintenance overhead, accuracy failures, and the hidden cost of employee adoption.
MCP Explained: What It Means for AI Application Architecture
Anthropic's Model Context Protocol is becoming the standard way to connect AI models to external tools and data sources. This is what it actually changes about how you build AI applications.
AI Agents in Production: Lessons From Deploying 10+ Agentic Systems
Everyone's excited about agents. Far fewer have shipped them to production. This article documents the failure modes, reliability patterns, and design decisions that separate working agentic systems from demos.
More articles coming soon
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