Aaron T. E. Raymer

I've built AI-augmented development workflows from zero inside real engineering orgs. Custom LLM tooling, agentic coding systems, structured context architectures, RAG pipelines. But LLMs are one tool in a very large toolbox. Depending on your problem, the right answer might be a transformer, a CNN, a gradient-boosted classifier, a recommendation engine, a time-series forecasting model, a custom fine-tune, or something you haven't heard of yet. And sometimes the right answer isn't a model at all. Sometimes what you actually need is better data infrastructure, a well-designed heuristic, or an engineering process that isn't fighting itself.

I'll tell you the difference, even when the honest answer is less exciting than the AI pitch. I help companies identify where AI and ML actually create leverage and ship the right solution instead of chasing whatever the hype cycle is selling this quarter.

Most engineering orgs don't fail because the engineers aren't talented. They fail because the org chart, the technical architecture, and the business strategy are all operating on different wavelengths. I've seen this pattern enough times to recognize it quickly. Teams organized around technical layers instead of business domains. Ownership so diffuse that nothing ships without five teams coordinating. Architecture that evolved to mirror an org structure nobody planned.

I restructure engineering orgs to fix this: stream-aligned teams, clear ownership boundaries, platform thinking applied where it actually helps. I've taken delivery predictability from the mid-60s to the mid-80s by redesigning team structures and the SDLC together, not by adding process on top of a broken foundation.

I build the internal platform and DevEx foundations that let engineering teams move fast without breaking things. Trunk-based delivery, observability, DORA metrics, hexagonal architecture, CI/CD. The infrastructure layer that makes everything else possible.

Just as important: I help companies find the right amount of platform for where they are right now. A 60-person startup doesn't need the same platform investment as a 600-person company, and over-building too early creates its own kind of drag. I right-size the platform so it solves today's pain without boxing you in for tomorrow's growth.

I help companies understand what agile actually is (and isn't) and restructure their end-to-end software delivery lifecycle for efficiency and repeatability. How product and engineering collaborate in discovery. How work flows through teams. How you know whether the system is healthy. I redesign the whole pipeline so shipping becomes predictable instead of heroic.

I won't promise you a magic metric that captures "engineering productivity," because that metric doesn't exist and anyone selling you one is not being straight with you. What I will do is build visibility into delivery health: cycle time, throughput, deployment frequency, failure rates. The signals that tell you whether your system is improving or decaying, even when individual output is hard to quantify.