AI Development
Ship AI systems with speed, accuracy, and control.
Build, evaluate, and deploy AI pipelines with reproducible training data, local models, and measurable performance improvements.
Focus areas
Model evaluation + drift checks.
Embedding pipelines + RAG.
Resource indexing + governance.
Development stack
Unified AI layer (local + remote models).
Evaluation harness for accuracy and latency.
Embedding + vector search pipelines.
Automated sync with model registries.
AI enhancement loop
Iterate on accuracy and speed by improving data quality, tightening retrieval, and validating responses against indexed sources.
Related hubs
Connect AI development to courses, datasets, and evidence tracking.
AI development FAQs
What counts as an AI enhancement? Improvements to accuracy, latency, reliability, and evidence traceability.
How do I validate performance? Use /pipeline validation, compare outputs to indexed sources, and track drift.
Can I run everything locally? Yes. Local models are preferred, with optional remote fallbacks.
Where do I start? Index training data, then open the AI development resources hub.