Local AI setup
Install, configure, and document local AI tools so a team can run them again without guessing paths, models, or runtime commands.
Ai Embedded Systems helps teams plan, repair, and build local AI workflows, model-training prep, RAG systems, and robotics or embedded AI prototypes. The work stays tied to files, tests, hardware limits, and reviewable outputs.
Install, configure, and document local AI tools so a team can run them again without guessing paths, models, or runtime commands.
Prepare datasets, choose a training lane, write evaluation checks, and define what a useful adapter or fine-tune must prove before more money is spent.
Build source-backed retrieval flows, document corpora, answer gates, and review paths so assistants do not answer from stale or missing context.
Review AI, data, or robotics repos for broken install paths, weak tests, unclear runtime state, missing handoffs, and risky hidden assumptions.
Turn repeated AI work into scripts, app flows, checklists, or internal tools that keep files and decisions traceable.
Plan perception, local reasoning, logging, control, and hardware-aware software paths before a prototype absorbs time and parts budget.
We start with the current files, hardware, model goals, and constraints. The first useful result is a short path from current state to the next testable step.
If the scope is clear, we create the setup, dataset, script, page, workflow, or repo change and leave receipts the team can check.
For model work, we focus on source material, training records, eval records, and hardware fit before claiming a model is ready.
Shawn handles software and AI scope. Robert reviews robotics and physical system scope when the work touches sensors, movement, or platform planning.
This static form opens your email client and addresses both founders. Include the current system, what you want to change, what hardware you have, and any deadline or budget limit.