Insights
Both frameworks can do the job. The real decision factors are async streaming needs, admin tooling, and how much of the project is 'AI feature' versus 'ordinary CRUD app with AI in one corner.'
Transcription is the easy part. The real engineering challenge in AI clinical documentation is turning a natural conversation into a review-ready record a clinician will actually trust and sign.
Keyword-matching resumes against a job description isn't AI screening — it's search with extra steps. What actually separates useful AI-assisted hiring from resume-parsing theater.
Shared database, separate schemas, or separate databases per tenant? A practical breakdown of multi-tenant data isolation strategies from building a multi-tenant AI hiring platform.
The gap between a ChatGPT-style demo and a production real-time AI interviewer is bigger than it looks. Notes from building live video/audio AI agents on LiveKit and OpenAI's Realtime API.
A practical framework for deciding whether your operational bottleneck calls for an AI automation layer, a custom software build, or both — from a consultant who's shipped both.
Lessons from real ERP and internal systems integration work — where these projects typically fail, and the architectural decisions that prevent it.
The architectural decisions that separate full-stack applications that age well from ones that require a rewrite — from database design to frontend performance.