How the Context Engine Makes Your AI Summaries Smarter

A single meeting summary is useful. But a summary that understands your team's last 20 meetings? That's transformative.

The Problem with One-Shot Summaries

Most AI meeting tools treat each meeting as an isolated event. They transcribe, summarize, and move on. But real work doesn't happen in isolation — decisions evolve, topics recur, and context matters.

How the Context Engine Works

OTLDR's context engine accumulates knowledge at the series level. Every time a meeting is summarized, the AI also updates the series context — capturing recurring themes, tracking decisions over time, and learning your team's patterns.

What Gets Accumulated

• Recurring topics — Issues that keep coming up across meetings
• Decision evolution — How decisions change and develop over time
• Team dynamics — Who typically handles what, participation patterns
• Terminology — Team-specific jargon and project names

Real-World Example

Consider a "Dev Team Weekly" series with 24 past meetings. When the AI summarizes meeting #25, it doesn't start from scratch. It knows:

• The team has been tracking a "WebSocket optimization" issue for 3 weeks
• Minjun typically handles backend performance work
• The deployment pipeline was discussed in meetings #20-#23 and resolved in #24

This context makes the summary more relevant, accurate, and actionable.

Privacy by Design

All context stays within your workspace. The context engine uses your own AI API keys, and accumulated knowledge is never shared across workspaces or used for AI training.

Try It

Start with any series and watch your summaries improve over time. Get started free.