Short answer: use an external speech-to-text service, then send the transcript to a multi-model gateway for summarization. A single key sounds cleaner, but it is the wrong selection criterion when the audio capability itself is not available; the useful boundary is “transcript text in, evaluated summary out.”
Capability first.
That split preserves the part that actually reduces integration work. Your STT adapter produces text once, while the summarization side can choose among OpenAI, Claude, Gemini, and other chat models without forcing the audio pipeline to know which model wrote the final summary.
It also gives the experiment a hard constraint: don't call the architecture successful because one sample meeting produced a plausible paragraph. Check model availability first, then score factual coverage, unsupported claims, structured-field validity, latency, and token use on a fixed transcript set.
Why does one key fail as the main design goal?
