Generic transcription tools constantly ruin meeting records by misspelling executive names, miscapitalizing brands, and completely missing your internal company jargon. AI-Transcript solves this by automatically harvesting key vocabulary from your transcripts, Jira boards, and business documents. By filtering and applying only the most relevant 30–40 terms per session, it delivers flawless, organization-aware Transcripts and Minutes of Meeting (MoM) that you can actually trust.
| Component | Description | Examples / Application |
|---|---|---|
| The Core Problem | Generic AI systems misspell names, lower-case proper brands, and fail to understand unique internal business jargon, destroying transcript and MoM credibility. | • "My Daily" instead of Mike Daly • "nvidia" instead of NVIDIA • "ai" instead of AI |
| Language Categories Managed | 1. Keywords: People, clients, projects. 2. Terms: Industry, tech, and tool names. 3. Jargon: Internal shorthand and process terms. |
• John Smith, Acme Corp • NVIDIA, Microsoft Azure • Project Alpha, Sprint 15 |
| Harvesting Sources | 1. Initial AI Extraction: Identifies phonetic errors. 2. Jira Integration: Syncs live project data. 3. Document Mining: Reads PDFs and reports. |
• Corrects contextual errors. • Extracts tasks and labels. • Learns domain vocabulary. |
| The Processing Filter | Relevance Over Volume: Overloading with terms causes errors. The engine filters, ranks, and dynamically applies a highly focused set of 30–40 terms per meeting. | • Filters out engineering jargon during a Sales meeting. • Prioritizes revenue terms for sales pipeline reviews. |
| The Ultimate Outcome | High-credibility, organization-aware Transcripts and Minutes of Meeting (MoM) that require minimal human cleanup. | Accurate, professional, and trusted documentation. |