Transcript cleanup and topic segmentation across 24 interviews is solid AI territory.

The same cleaning rules (filler removal, speaker label correction, timestamp standardization) and segmentation schema (pain points, budget, decision-making) apply uniformly across all 24 transcripts. Structure is consistent enough that a single prompt pipeline can process each file the same way.

Filler removal and formatting are crisp, but topic segmentation requires judgment about overlapping themes — a respondent discussing budget constraints may simultaneously be describing pain points. The four topic categories are defined, but boundary cases will arise and the agent needs clear rules for handling them.

The agent needs the audio files or rough transcripts as input, which the user already has. Standard LLM pipelines with file ingestion can handle long transcripts in batches, and JSON output is a native capability. No external APIs or live data are required.

Errors in transcript cleaning are visible and correctable — the user can review outputs against originals. Misclassified segments are annoying but not irreversible, and no sensitive downstream decision hinges on a single labeling mistake.

Speaker label correction may require contextual inference when auto-transcription is ambiguous, and nuanced topic segmentation (e.g., a comment that spans budget and decision-making) benefits from human review. A final human pass on a sample of transcripts is advisable but the bulk of the work is automatable.