Voice AI: Beyond Transcription with Granola, CoLoop & EdgeTier

2026-05-11 · Source: AssemblyAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

A panel discussion featuring representatives from Kodoop, Edge Tier, and Granola, moderated by Assembly AI, explored the practical applications and challenges of Voice AI in various business contexts. Kodoop uses Voice AI to transcribe qualitative research interviews for customer insights, focusing on precise terminology and speaker diarization in complex domains like pharma. Edge Tier, a conversational intelligence platform for high-volume contact centers, ingests and processes calls, emails, and chats, using Voice AI for transcription to identify customer friction and agent performance issues at scale. Granola employs Voice AI for real-time and asynchronous transcription of meetings to generate notes. The panelists discussed their end-to-end Voice AI pipelines, emphasizing post-processing for accuracy, the critical importance of speaker identification, and the trade-offs between real-time and post-call processing. Key challenges highlighted include achieving high transcription quality in low-resource languages, handling mixed-language conversations, and managing noisy audio environments.

Key takeaway

For AI Architects and NLP Engineers building Voice AI solutions, prioritize robust speaker identification and post-transcription processing tailored to specific domain terminology. While real-time processing offers immediate user feedback, near-time processing with rapid post-call analysis can still deliver timely, actionable insights for high-volume data. Invest in flexible UI/API layers to ensure end-users can easily query and derive value from the processed data, as transcription quality directly impacts downstream analytical capabilities and user trust.

Key insights

Voice AI adoption in business prioritizes transcription accuracy and speaker identification, with post-processing crucial for domain-specific contexts.

Principles

• Contextual data enhances transcription accuracy.
• Speaker identification is critical for actionable insights.
• UI/UX design is vital for data accessibility.

Method

Voice AI pipelines involve raw data ingestion, unified formatting, post-processing for semantic enrichment, and UI/API exposure. LLMs are used for transcript correction and contextual analysis.

In practice

• Augment transcription with project-specific keywords.
• Use LLMs to correct phonetic mistranscriptions.
• Implement language-per-message detection for multilingual calls.

Topics

• Voice AI Pipelines
• Speaker Diarization
• Post-Transcription Augmentation
• Multilingual Speech Recognition
• Real-time Voice AI

Best for: AI Architect, NLP Engineer, AI Engineer, Machine Learning Engineer, Director of AI/ML

Related on AIssential

• The Future IS Speaker Identification 🚀 — Real-time speaker identification and diarization are critical for advancing voice technology capabilities.
• Post-call processing with CoLoop 🛠️ — Contextual information from discussion guides significantly improves transcription accuracy via LLM-driven post-processing.
• Angelos Perivolaropoulos of ElevenLabs at RAAIS 2026 — Real-world voice AI demands balancing high speech-to-text accuracy with critical low-latency performance for effective interactive systems.
• Speech intelligence for enterprises - Mistral AI — Mistral AI provides enterprise-grade speech intelligence with advanced text-to-speech and speech-to-text models for diverse applications.
• Introducing Post-Stream Refinement: Higher-Accuracy Real-Time Transcription — Post-Stream Refinement enhances real-time speech recognition by combining instant partial results with a higher-accuracy, context-aware second pass.
• Why Emotion Matters More Than Sound — Building production-grade voice AI agents requires overcoming significant application-layer challenges beyond basic LLM integration.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AssemblyAI.