Automate Transcription and Action Item Extraction from Meetings

Many teams start with tools like Otter.ai or Fireflies.ai. They generate accurate transcripts and basic summaries, but their action item detection is generic. The AI identifies phrases like "I'll follow up on that" but cannot understand the context specific to your business. It doesn't know that "update the Q3 roadmap doc" refers to a specific Confluence page or that a bug mentioned by name needs to be linked to a specific Jira ticket.

Consider a 15-person product team that uses Fireflies.ai integrated with Asana. After a weekly sync, Fireflies creates five new, unlinked tasks in a default project. The project manager now has to manually re-listen to parts of the 60-minute recording to add the correct assignees, link tasks to parent epics, and copy-paste relevant context from the transcript. The automation creates more organizational debt instead of reducing it, adding 30 minutes of cleanup work per meeting.

The structural problem is that these off-the-shelf products are built for horizontal, mass-market use. Their data models are fixed and cannot be trained on your internal jargon, project names, or specific workflow rules. They treat your project management tool like a dead-end drop box. They cannot perform conditional logic, update existing tasks, or query your system for context before creating an item. You need a system that operates with the same context as your team members.

The engagement would start by auditing your current meeting and project management workflow. Syntora maps where recordings are stored (Zoom Cloud, Google Drive), how projects are structured in your tools like Asana or Jira, and what a perfectly formatted action item contains. This discovery phase defines the required custom fields, assignee logic, and summary format before any code is written.

The technical approach uses a serverless pipeline on AWS Lambda, triggered whenever a new meeting recording is saved. The audio file is processed by a transcription API. The resulting text is then fed to a Claude API model with a prompt engineered to understand your business context and project-specific language. We use the Claude tool_use feature to force the output into a strict JSON schema that directly matches the API requirements of your project management tool. This ensures every action item is created correctly the first time.

The delivered system runs invisibly in the background. Within 15 minutes of a meeting ending, a summary is posted to Slack and action items appear as correctly formatted, assigned, and linked tasks in Asana. You receive the full Python source code, a runbook for updating prompts, and a dashboard to monitor processing. Hosting costs for this pipeline are typically under $50 per month.