Accurate transcription is not just about hearing words correctly.
It is about understanding what those words represent.
Names, terminology, and internal jargon carry meaning.
If they are wrong, the entire transcript—and the resulting Minutes of Meeting—loses credibility.
Transcript credibility is important for us, and here is how we achieve it with AI Transcript.
No organization wants to distribute Minutes of Meeting where:
These are not minor errors. They undermine trust in the entire document.
Names must be:
Even small deviations can change meaning or create confusion.
In business communication, formatting carries meaning.
For example:
- “AI” is not the same as “ai”
- “Microsoft” is not “microsoft”
Incorrect capitalization signals lack of accuracy and reduces confidence in the output.
High-quality transcripts standardize these automatically—but only when the system knows what to look for.
To achieve reliable transcription, three distinct categories must be managed:
1. Keywords (Primarily Names)
These include:
- People (e.g. “John Smith”, not “john smith”)
- Clients
- Projects
- Products
Keywords define who and what the conversation is about.
2. Terms (Standard Industry or Technical Language)
These include:
- Company names (e.g. “NVIDIA”, not “nvidia”)
- Technologies
- Tools and platforms
- Industry-standard terminology
These terms ensure the transcript reflects professional and technical accuracy.
3. Company Jargon (Internal Language)
Every organization has its own language:
- Internal project names
- Abbreviations
- Team-specific shorthand
- Process terminology
This is often the hardest for generic AI systems to interpret correctly.
Capturing this correctly is what separates generic transcription from organization-aware transcription.
Keyword accuracy is not manual—it is built through a structured process.
Step 1: AI Extraction from Transcript
When audio is converted into a transcript, AI analyses the text to:
- Detect words that may be incorrect
- Identify likely names, terms, and entities
- Suggest corrections based on context
For example:
- “my daily” → likely “Mike Daly”
- “nvidia” → “NVIDIA”
These suggestions are then presented for human validation in the HITL process.
Step 2: Enrichment from Jira
We integrate with Jira to extract:
- Project names
- Task references
- Internal terminology
- Frequently used labels
This allows the system to align transcription with how your organization actually works.
Step 3: Extraction from Documents (e.g. PDFs)
We analyze internal documents such as:
- Project documentation
- Reports
- Technical specifications
From these, we extract:
- Key terms
- Domain-specific vocabulary
- Repeated language patterns
This expands the system’s understanding beyond a single meeting.
It is easy to generate hundreds of keywords.
However, overloading the system creates the opposite effect:
- Increased ambiguity
- Incorrect substitutions
- Reduced accuracy
In practice, AI performs best when working with a focused, relevant set of terms.
To maintain accuracy, we:
1. Rank Keywords by Relevance
Keywords are prioritized based on how frequently they appear in the transcript and related data sources.
2. Classify Meeting Types
Each transcript is assigned a meeting type, such as:
- Weekly Sales Review
- Product Development Meeting
- Project Follow-Up
3. Select the Most Relevant Keywords
Instead of using all available terms, we:
- Select a focused set (typically 30–40 keywords)
- Match them to the meeting type
- Prioritize the most relevant and frequently used terms
For example:
A Weekly Sales Meeting will prioritize:
- Client names
- Revenue terms
- Sales pipeline language
While excluding unrelated terms from:
- Internal engineering projects
- Technical development discussions