Technical AI systems
Technical AI systems for work a prompt won't solve.
Some projects need model work, data plumbing, analysis tools, and someone willing to sit with the technical details until they make sense.
When prompts won't cut it
Where the hard part lives in the details
- The work involves research data, simulation, signal processing, or analysis pipelines.
- A prototype exists. It needs to become something a team can actually use.
- The problem needs ML, but the real work is the data and the evaluation.
- The team wants a technical reviewer before committing to a bigger build.
Working method
Keeping the build honest
- Get clear on the analysis goal and the failure modes.
- Review data quality, tooling, and evaluation methods.
- Build the smallest useful pipeline or interface first.
- Write down assumptions, tradeoffs, and handoff requirements as we go.
Background
Where I come from
Physics PhD. PROSPECT neutrino experiment at Oak Ridge. Detector analysis, ML for event reconstruction, scientific computing. That training matters when "plausible" isn't good enough.
Areas I check early
- 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 doesn't run ahead of the problem.
Talk through the technical problem