📊 Accuracy by Feature
📝 Transcription
95-99%
Word accuracy
Clear audio, native speakers
📋 Summaries
80-90%
Key point capture
Main topics & decisions
✅ Action Items
85-92%
Task detection
Explicit assignments
🗣️ Speaker ID
90-98%
Voice matching
With voice training
🏆 Accuracy Leader Board
| AI Tool | Transcription | Summary | Actions | Overall |
|---|---|---|---|---|
| Gong | 98-99% | 90-95% | 92-95% | 95% |
| Otter | 96-98% | 85-90% | 88-92% | 92% |
| Avoma | 95-97% | 88-92% | 90-94% | 91% |
| Fireflies | 94-96% | 85-88% | 88-90% | 89% |
| tl;dv | 93-95% | 82-86% | 85-88% | 87% |
| Supernormal | 92-95% | 80-85% | 86-89% | 86% |
*Based on independent testing with clear audio, business meetings, native English speakers
🎨 Factors Affecting Accuracy
✅ Accuracy Boosters
- 🎤 High-quality microphones
- 🔇 Quiet environment
- 👥 Clear speaker turns
- 🗣️ Native language speakers
- 📹 Video meetings (lip reading)
- 🎯 Structured meetings
- 📝 Agenda provided
❌ Accuracy Killers
- 🔊 Background noise
- 🌐 Heavy accents
- 🗣️ Multiple simultaneous speakers
- 📞 Phone dial-ins
- 🎵 Music or echo
- 💬 Technical jargon
- 🌍 Code-switching languages
📈 Accuracy by Meeting Type
📊 Sales Calls
93-97%Structured conversations, clear next steps, explicit assignments
🎯 1-on-1 Meetings
92-96%Clear speakers, focused topics, minimal crosstalk
👥 Team Standups
88-92%Quick updates, some overlapping speech, familiar voices
🌐 All-Hands
80-85%Large groups, Q&A sessions, varying audio quality
🎉 Brainstorming
75-82%Chaotic discussion, multiple speakers, informal structure
📝 Real Accuracy Examples
Example 1: Action Item Detection
What was said:
"Sarah, could you maybe follow up with the client about that proposal we discussed? I think by Friday would be good."
AI detected:
✅ @Sarah: Follow up with client about proposal - Due: Friday
Accuracy: 100%
Example 2: Summary Quality
10-minute discussion about:
- Q3 revenue targets
- New pricing strategy
- Competitor analysis
- Team expansion plans
AI summary captured:
- ✅ Q3 target: $2M (15% growth)
- ✅ New tier pricing approved
- ✅ Main competitor: TechCorp
- ❌ Missed: 2 new hires planned
Accuracy: 75% of key points
Example 3: Technical Terms
Transcription accuracy for jargon:
| Kubernetes → ✅ Kubernetes | 100% |
| PostgreSQL → ⚠️ Postgre SQL | 90% |
| CI/CD pipeline → ✅ CI/CD pipeline | 100% |
| Kafka queues → ❌ Calf-ka cues | 40% |
🚀 Tips to Maximize Accuracy
🎤 Audio Quality
- Use headsets or dedicated mics
- Minimize background noise
- Test audio before meetings
- Avoid speakerphone when possible
🗣️ Speaking Best Practices
- State names when assigning tasks
- Speak one at a time
- Spell out technical terms once
- Summarize key decisions clearly
⚙️ Tool Configuration
- Complete voice training setup (Otter, Fireflies)
- Add custom vocabulary for your industry
- Connect calendar for participant names
- Review and correct AI outputs to train the system
- Use meeting templates for consistent structure
🛡️ Accuracy Guarantees & Limits
✅ What AI Gets Right
- Explicit statements and decisions
- Clear action items with owners
- Numbers and dates mentioned
- Main topics and themes
- Questions asked directly
⚠️ What AI Might Miss
- Subtle implications or hints
- Non-verbal communication
- Sarcasm or humor context
- Visual whiteboard content
- Side conversations
⚡ Remember: AI summaries are assistants, not replacements!
Always review AI output for critical meetings. Most tools allow easy editing and corrections to ensure 100% accuracy for important records.
🔮 The Future: Even Better Accuracy
Coming Soon to AI Meeting Tools:
- 🧠 GPT-4+ integration (99%+ accuracy)
- 👁️ Visual context understanding
- 🌍 Better multilingual support
- 🎯 Industry-specific models
- 😊 Emotion and tone detection
- 🔄 Real-time correction learning
- 📊 Confidence scoring for summaries
- 🤝 Multi-modal understanding