AI Systems Positioning

What teams usually need from AI work

Not just a model that can answer. A system that can behave.

Most teams think they are hiring someone to build AI systems. What they often actually need, and usually discover too late, is someone who can make those systems behave in the real world.

That sounds like a small difference until you live inside it.

Start a conversation Questions worth asking
Back to home

Why teams get this wrong

A surprising number of AI projects do not fail because the model is weak. They fail because the task was never properly bounded in the first place. Or because memory was added as a feature instead of justified as a behavior improvement. Or because tool use looked impressive in a demo and then broke quietly in production. Or because the evaluation framework produced clean numbers that had very little to do with whether the system was trustworthy once people depended on it.

Teams end up optimizing average-case capability while the real cost comes from worst-case behavior: quiet failures, uncontrolled widening, and confidence without grounding. The gap between prototype and production keeps widening because the system layer is treated as an afterthought.

What I actually work on

I focus on turning vague requests into bounded, testable specifications. I design for behavioral consistency, not just one clean demo. I want tool use to be reliable, memory to be useful, and failure to be visible, predictable, and recoverable.

That means measuring whether outputs are trustworthy, not just technically plausible. It means closing the gap between something that works in a notebook and something a team can ship without crossing its fingers.

Questions worth asking

If these feel familiar, you are already working on the layer that usually decides whether an AI system can be trusted.

  • How do you distinguish model failure from orchestration failure?
  • How do you know memory is improving outcomes rather than just increasing context size?
  • What happens when the system chooses the wrong tool?
  • How do you measure consistency under repeated identical conditions?
  • What is your strategy for detecting when the system is confidently wrong?
  • How well do your evaluation metrics predict actual business outcomes?

Most ML engineers are very good at getting models to work.

I have been focused on getting systems to behave.

If your team is already asking questions like these, we should probably talk.

If those questions already have clear, measurable answers, you probably have strong coverage in this layer. If they do not, that is likely where I can help.

I work on the layer between AI capability and operational trust.

Start a conversation Explore Mímir