Danielius Studio

AI integrations (LLM, RAG, voice)

AI Transcription & Segmentation Pipeline

Cuts 200 multi - client Zoom calls into clean per - client, per - question Markdown ready to drop into your existing OpenAI vector store.

Zoom Recordings ~200 calls, 1-3 hrs each Audio Storage cloud blob staging Job Dashboard monitor 200-batch run AssemblyAI API transcription + diarization Cost Monitor AssemblyAI + GPT spend Webhook Handler async completion Raw Transcripts speaker-labeled JSON Retry Queue failed job replay Speaker Diarization split A/B/C labels Identity Resolver name extraction Client Segmenter group by person Confidence Scorer flag ambiguous Question Detector split Q&A pairs Human Review low-confidence calls Q&A Cleaner strip filler, polish LLM Reasoner GPT-4 segmentation Markdown Formatter apply spec template Markdown Repository structured output OpenAI Embedder vectorize chunks Vector Store your existing system Knowledge Retrieval your existing system LLM Response Gen your existing system audio uploaded stored tracked tracks spend fires webhook failures retried stores parse JSON labeled chunks extract names names scored per-client text flag for review questions uses GPT-4 clean Q&A writes MD ingest vectors queries context

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