Blog
Notes from inside the deployment
Field notes from real engagements, written by the engineers who were in the room.
AI strategy · Aug 3, 2026 · 5 min read
Our Client Wanted a Chatbot. We Built Their Quote Desk Instead.
The most valuable AI work in most companies doesn't look like AI. A story about a distributor, a few hundred daily quotes, and why no vendor was ever coming to help.
AI engineering · Jul 29, 2026
How We Do Forward Deployed Engineering at SIEL
For us, forward deployed engineering is not a job title. It is the only delivery model we have found that gets an AI system from pilot into real operations and keeps it running after we go.
AI engineering · Nov 26, 2025
Why 2026 Kills Coding, But Not Engineering
LLMs are getting remarkably good at writing code. But software engineering is everything around the code. Here is why engineers who have mastered judgment, systems thinking, and communication are not going anywhere.
Agentic AI · Sep 4, 2025
5 Architectural Patterns for Building Reliable AI Agents
Most generative AI pilots never create business value. Not because of the models. Because of an architectural learning gap. Five patterns that separate production systems from demos.
AI strategy · Jul 8, 2025
It's a Mindset Problem, Not the AI
After years helping businesses through AI transformation, one truth has become clear. The biggest barrier is not the tools or the technology. It is how we think. And it is costing teams time, money, and trust.
AI engineering · Mar 12, 2025
Why MCP Has Become the Talk of the AI Town
Model Context Protocol promises a USB-C moment for AI integrations. Is it just another buzzword, or does it actually solve the integration problem that has been holding back complex AI deployments?
AI models · Jan 28, 2025
Is DeepSeek Worth the Hype?
DeepSeek R1 activates only 37 billion of its 671 billion parameters for any given task. What this efficiency breakthrough actually means for businesses building with AI, and why it changes the economics entirely.
AI models · Nov 19, 2024
Smarter beats bigger: where language models go next
Ilya Sutskever declared the age of scaling is over. Something different is happening in GenAI that challenges the "more parameters mean better performance" assumption. What this inflection point means for those building AI systems.