Post-call processing with CoLoop 🛠️

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

Summary

CoLoop enhances post-transcription accuracy and domain specificity by leveraging project-specific contextual information. When users create a project in CoLoop for qualitative research, the platform extracts structured data, such as keywords, from discussion guides. This context is initially passed to AssemblyAI for improved transcription. After the initial transcription, an AI coding agent, powered by a large language model (LLM), performs a second pass over the transcript. This agent makes targeted edits, correcting phonetic mistranscriptions, joining words, and addressing other inaccuracies based on predefined instructions. CoLoop validates this process using an eval dataset, where clean transcripts are intentionally corrupted to test the correction mechanism's effectiveness.

Key takeaway

For qualitative researchers aiming to improve the accuracy and domain relevance of interview transcripts, integrating project-specific context like discussion guides into your transcription workflow is crucial. By leveraging an LLM-powered post-processing step, you can automate corrections for phonetic errors and domain-specific terminology, significantly reducing manual cleanup time and enhancing data quality for analysis. Consider building an evaluation dataset to continuously validate and refine your automated correction pipeline.

Key insights

Contextual information from discussion guides significantly improves transcription accuracy via LLM-driven post-processing.

Principles

• Contextual data improves transcription.
• LLMs can act as AI coding agents.

Method

Extract structured keywords from discussion guides, pass to initial transcription service, then use an LLM-powered AI agent for post-transcription edits based on instructions.

In practice

• Use discussion guides for context.
• Implement LLM agents for transcript correction.

Topics

• CoLoop Platform
• Post-Transcription Processing
• AI Coding Agent
• Language Model Workflows
• Transcript Correction

Best for: Machine Learning Engineer, NLP Engineer, AI Engineer, Data Scientist

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