Applied AI · 8 min read
AI App Development: Applied AI That Earns Its Place in the Product
LLM features, recommendation systems, computer vision and the evaluation work that separates a shipped AI product from a demo.
Start from the job, not the model
The useful question is which repeated user task is slow, ambiguous or manual today. AI belongs where it measurably shortens that task — search and ranking, summarization, classification, extraction, generation of a first draft a human then edits.
Architecture that survives model churn
Keep the model behind an interface. Retrieval, prompt assembly, tool calls and post-processing are your product; the model underneath should be swappable when a better or cheaper one appears, which it will.
Evaluation is the deliverable
A labelled evaluation set, offline scoring, and online guardrails with human review for the highest-risk paths. Without evaluation you cannot tell a prompt improvement from a regression, and every release becomes a guess.
Cost, latency and privacy
Token cost and response latency are product constraints. Caching, smaller task-specific models and streaming responses usually matter more to perceived quality than a larger model does. Data handling and retention need a documented answer before launch.
Email business@wvelabs.com or call (800) 588-9094. Tell us what you want to build and when you want to be live, and we will come back with a phased plan, a budget range and the team who would build it.
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