transcript-analyzer

You can extract decisions and actions from meeting transcripts by processing the text through chunk-based NLP analysis, which identifies and categorizes key discussion points. This approach returns structured JSON arrays containing actions, opinions, and source snippets for faster follow-ups.

Yes, you can generate a terminology glossary from interview transcripts using NLP extraction. The analysis identifies specific terms discussed during the meeting and outputs them alongside structured extractions, which accelerates team onboarding and standardizes project vocabulary.

Yes, transcript analysis works with OpenAI dependencies and supports configurable models such as llama-3.3-70b or Cerebras-based APIs. This flexibility allows you to process meeting discussions and extract structured data while tracking token usage for auditing.

The best way to structure meeting notes for downstream tooling is to parse transcripts into categorized JSON arrays. This format captures decisions, actions, and questions with corresponding speaker tags and source snippets, making the data immediately consumable by other systems.

Chunk-based processing handles long project discussions by breaking the transcript into manageable segments before analysis. This method ensures comprehensive extraction of questions and opinions while maintaining context, and it reports total token usage so you can audit the operation.

A key limitation when extracting opinions from meeting transcripts is the dependency on the configured AI model's accuracy. Additionally, while the output includes speaker and source snippets, complex contextual nuances in project discussions may require manual review to ensure correct categorization.