Technical AI systems

Technical AI systems for work that needs more than a prompt.

Some projects need model work, data plumbing, analysis tools, and judgment from someone comfortable with scientific and engineering details.

Talk through the technical problem Ask about technical scope
Good fit

When the hard part is technical detail

  • The work involves research data, simulation, signal processing, or analysis pipelines.
  • A prototype exists but needs to become a usable tool.
  • The problem needs ML, but the data and evaluation are the hard part.
  • The team needs a technical reviewer before committing to a larger build.
Approach

How I keep the build honest

  1. Clarify the analysis goal and failure modes.
  2. Review data quality, tooling, and evaluation methods.
  3. Build the smallest useful pipeline or interface.
  4. Document assumptions, tradeoffs, and handoff requirements.
Example

Background

My research background is in physics, detector analysis, machine learning, and scientific computing. That matters when the system has to be correct, not just plausible.

What I would measure first

  • Data quality issues
  • Model evaluation criteria
  • Analysis throughput
  • Review and reproducibility needs

Talk through the technical problem

For ML, research software, or technical analysis work, start with a focused advisory conversation so the build does not outrun the problem.

Talk through the technical problem