What is a research pipeline?

A research pipeline is an end-to-end system that discovers, filters, organizes, reviews, and distributes knowledge - with minimal manual effort. Curyloop gives you all the building blocks to create one.

The pipeline stages

Stage 1: Discovery (automated)

Set up your AI agent to continuously find relevant content:

• Configure topics aligned with your team's focus areas
• Connect sources that your industry relies on
• Schedule the agent to run daily or weekly

At this stage, content flows in automatically. Your agent does the heavy lifting of scanning hundreds of sources.

Stage 2: Triage (semi-automated)

Not everything the agent finds is worth your team's time. Triage quickly:

• Review agent-discovered items in your inbox
• Like items that deserve team attention
• Skip or remove low-quality finds
• Add notes to items that need context

Tip: Assign a rotating "triage" role to team members. One person spends 10 minutes each morning reviewing agent finds.

Stage 3: Curation (manual)

Supplement AI discoveries with human-found content:

• Team members add items via the browser extension throughout the week
• Encourage notes explaining why something is worth reading
• Use tags consistently to keep items organized

The best pipelines combine automated discovery with human judgment. AI finds the volume; humans provide the nuance.

Stage 4: Review (collaborative)

Bring the team together to review curated content:

• Run a weekly discovery session
• Review the most-liked items first
• Mark items as discussed and capture decisions
• Generate an AI summary for the record

Stage 5: Distribution (automated)

Share insights with the people who need them:

• Share sessions with external stakeholders
• Send Telegram digests to keep the team informed
• Export summaries for presentations or reports
• Archive completed sessions for future reference

Example pipeline: Product team

Scaling your pipeline

As your team grows, consider:

• Multiple groups: Separate pipelines for different focus areas (market research, competitor tracking, technical trends)
• Cross-group sessions: Pull the best items from multiple groups into a monthly leadership digest
• n8n automations: Trigger actions when items are added or sessions are completed

Next steps

• Fine-tuning AI Relevance to improve discovery quality
• Automation with n8n for advanced workflow triggers
• Remote Team Async Knowledge Sharing for distributed teams