Open Engineering standards.
We don't hide our methodology. We document how we approach software architecture, AI evaluation, database debugging, and project complexity so you know exactly how we work before you hire us.
How We Evaluate AI Systems
Raw trial-and-error prompting fails in production. Here is our step-by-step framework to test, validate, and measure LLM outputs programmatically.
Read Playbook Entry →How We Audit Rails Database Lockups
Slow queries aren't PostgreSQL limitations—they are data modeling bottlenecks. Here is how we analyze and fix lockups in high-traffic monoliths.
Read Playbook Entry →When to Say 'No' to Large Language Models
An LLM is a probabilistic engine. Before adding a model to your stack, see if a deterministic algorithm can solve it cheaper, faster, and with 100% reliability.
Read Playbook Entry →Estimating Complexity Without Meeting Overhead
We don't do daily standups or weekly estimation meetings. Here is our asynchronous design methodology to maintain velocity and ship code.
Read Playbook Entry →Resonate with our engineering standards?
Let's evaluate your software system and apply these exact principles to clean up your codebase.
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