I help SaaS teams move LLM features from prototype to production. The work starts with the real user workflow and a measurable definition of quality, then covers implementation, evaluation, failure handling, latency, cost control, and maintainability inside the existing product.
Typical engagements include integrating an AI capability into an existing SaaS application, reviewing a retrieval or RAG workflow, designing practical evaluation criteria, and turning an early prototype into a focused production milestone.
I prioritize reliable behavior over impressive demos. Before recommending an architecture, I clarify the available data, expected outputs, operational constraints, and the failure cases users will encounter. If a deterministic workflow is safer or cheaper than an LLM, I will say so early.
A good first step is a scoped technical discovery: map the workflow, identify the highest-risk assumptions, and define a small milestone with clear acceptance criteria.