Notes from an AI-native firm.
How we think about agents, outcomes and shipping fast without cutting corners.
Context, harness, loop: the three disciplines of agent engineering
Model choice gets the attention. The system around the model decides whether an agent finishes anything: what it knows, what it can do, and how it keeps going.
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Agentic SDLC: the software delivery model after AI assistants
AI assistants helped developers write code faster. An agentic SDLC changes who does the work, and what engineers are actually for.
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Why RayMish went all-in on AI-native delivery
After years of building products the traditional way, we rebuilt the firm around agents and outcomes. Here's the thinking.
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Stop buying chatbots. Start deploying agents that move metrics.
The difference between a demo and a production agent is the difference between a cost and a return.
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How we ship measurable AI outcomes in 30 days
Our playbook for compressing a 30-week SOW into a 30-day outcome, without cutting corners on security.
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Let's find your first 30-day outcome.
Book a discovery call. We'll map one workflow worth re-engineering and what shipping it looks like.