active project
AgScribe
LLM-driven structured information extraction over transcribed interviews, using private model deployment and an explicit target schema.
AgScribe grew out of ongoing LUM AI work in agriculture and qualitative research workflows. Publicly, the useful point is the system pattern: transcribed interviews are processed with privately deployed language models to produce structured outputs against defined schemas, making source material easier to inspect, compare, and reuse without naming the underlying customer engagements.
- Role
- System designer and applied scientist
- Period
- 2024-2025
- Audience
- research teams, applied NLP teams, domain experts