Capability: Meeting Processing

Capability: Meeting Processing

Transform meeting transcripts and notes into structured, actionable insights that connect to existing project context.

What It Is

Meeting processing takes raw conversational input — transcripts from meetings, pasted notes, recorded conversations — and extracts structured information that integrates with the project's context ecosystem.

The key distinction: meeting processing is not summarization. Summarization compresses a meeting into a shorter version of itself. Meeting processing connects new information to existing context — updating requirements, challenging hypotheses, resolving questions, surfacing contradictions.

A meeting summary answers "What happened in this meeting?" Meeting processing answers "What did this meeting change about what we know?"

Sources

Meeting content arrives through several channels.

Granola Meeting Notes

Granola captures meeting transcripts automatically with AI-enhanced note-taking. It provides structured meeting data through its MCP integration:

• query_granola_meetings — Search across meetings
• get_meetings — List recent meetings
• get_meeting_transcript — Get full transcript content

Manual Transcripts

Users paste or provide transcript files from other sources — Otter, Rev, manual notes, voice memos.

Pasted Meeting Notes

Users paste meeting notes directly into the conversation. These are typically less structured than transcripts but may contain the user's own annotations and highlights.

The Insight Extraction Pattern

Meeting processing follows a consistent extraction pattern regardless of the source.

Step 1: Read and Parse

Read the raw transcript or notes. Identify:

• Participants — Who was in the meeting
• Topics covered — Major subjects discussed
• Key statements — Specific things said that carry weight (quotes, decisions, commitments)
• Tone and dynamics — Was there disagreement? Enthusiasm? Hesitation?

Step 2: Classify Extracted Items

Every extracted item is classified by type:

Step 3: Connect to Existing Context

This is the step that distinguishes meeting processing from summarization. For each extracted item:

• Does it confirm something already in the context files?
• Does it contradict something already captured?
• Does it extend existing understanding with new detail?
• Does it resolve an open question?
• Does it create a new question?

Step 4: Present and Confirm

Show the user the extracted items with their classifications and connections. The user confirms before anything is written to context files.

From this meeting I extracted:

REQUIREMENTS (2 new):
- [Requirement A] — extends existing requirement in requirements.md about [topic]
- [Requirement B] — NEW, not previously captured

CONTRADICTIONS (1):
- [Item] contradicts the decision on [date] in decisions.md that said [X].
  The meeting participant said [Y] instead. Which is correct?

EVIDENCE (1):
- [Observation] — supports hypothesis version 3, specifically the claim about [Z]

QUESTIONS RESOLVED (1):
- [Question from questions.md] — resolved by [participant]'s statement that [answer]

Does this capture look right?

When to Use

Any companion that processes meeting content or conversational input.

Key Principle: Value Is Connection, Not Compression

The value of meeting processing is not making a long meeting shorter. It is making new information actionable by connecting it to what is already known.

A standalone meeting summary:

"Met with the customer. They want faster onboarding and better reporting. Action items: John will prototype the dashboard."

A connected meeting extraction:

"The customer's request for faster onboarding supports our hypothesis that activation speed is the primary driver of retention (hypothesis v2). Their reporting request contradicts our decision on 2026-02-01 to defer reporting to v2 — they described it as blocking a renewal. This resolves the open question about whether reporting is a retention factor (it is, at least for this customer segment)."

The second version changes what the team knows and believes. The first version is just a record.

The Contradiction-Flagging Pattern

The most valuable thing meeting processing can do is surface contradictions between new information and existing context. Contradictions are where learning happens.

Types of Contradictions

Hypothesis contradictions: New information challenges the current strategic hypothesis.

"Your hypothesis says users prioritize speed. This customer explicitly said they prioritize accuracy, even at the cost of speed."

Decision contradictions: New information suggests a previous decision was wrong.

"You decided to target enterprise customers. This meeting with three enterprise prospects revealed none of them see the value proposition. The small team customers in your pipeline are all enthusiastic."

Requirement contradictions: New information conflicts with a captured requirement.

"Requirements say the system must support offline mode. This stakeholder said 100% of their target users are always connected and offline support would add complexity they can't afford."

Internal contradictions: The same person said contradictory things in different meetings.

"In the January meeting you said [X]. In this February meeting you said [Y]. These can't both be true. Which reflects your current thinking?"

Handling Contradictions

Contradictions are never silently resolved. They are always surfaced to the user with:

1. What the existing context says
2. What the new information says
3. A clear statement that these conflict
4. Options for resolution (update the old, dismiss the new, refine to reconcile)

Processing Quality

The quality of meeting processing depends on the quality of the input.

The companion should gently encourage higher-quality inputs when low-quality inputs produce thin extractions: "I can only extract what's in the notes. For future meetings, a full transcript (Granola or similar) would let me catch the connections and contradictions that brief notes miss."

Anti-Patterns