Extracting decisions from meetings, clearly and accurately.
Meetings often contain important decisions buried among alternatives, opinions, questions, and discussion. Aeologic's Decision Extractor uses AI to identify what was actually decided, capture the context behind it, identify decision ownership, and flag tentative decisions for human confirmation.
In short
Aeologic built an AI agent that turns meeting transcripts, MOM documents, and discussion notes into a clear record of finalized decisions. It distinguishes decisions from options, opinions, and unresolved questions while capturing context, ownership, and conditional language.
- Industry Cross-Industry — Enterprises, Government Bodies & Organizations Running Structured Meetings
- Challenge Important decisions buried inside long meeting records
- Solution AI-powered decision detection, context capture and structured logging
- Deployment Cloud-based, API/interface-accessible AI agent
Important decisions were buried inside long meeting discussions.
Meetings often produce firm decisions, but those decisions are buried in long transcripts and discussion notes alongside options that were considered, opinions that were shared, and questions that stayed open. Without a clear record, teams can disagree later on what was actually decided, decisions may not get carried into downstream work, and someone has to re-read entire transcripts to confirm what was agreed.
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Decisions mixed together with alternatives, opinions, and open questions
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No reliable, searchable record of what was actually agreed
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Teams had to revisit entire transcripts to confirm decisions
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Tentative and conditional decisions could easily be mistaken for final decisions
What the Decision Extractor had to achieve.
Extract decisions from meeting transcripts, MOM documents, or discussion notes.
Distinguish finalized decisions from options considered, opinions, and unresolved questions.
Capture the context behind each decision, including what was decided and why.
Identify who made or approved each decision wherever that is stated or implied.
Flag decisions that sound tentative or conditional for quick human confirmation.
An AI agent that turns meeting records into a clear, usable decision log.
Flexible input handling
The agent accepts meeting transcripts, MOM documents, or Word/PDF discussion notes and normalizes them for analysis regardless of source format.
Decision detection
The agent identifies finalized decisions while separating alternatives considered, opinions shared, and questions that remain unresolved.
Context and ownership capture
Each decision is paired with its reasoning, relevant context, and available information about who proposed, made, or approved it.
Decision detection
Identifies finalized decisions from natural meeting language and consensus cues instead of relying only on explicit phrases such as "we have decided."
Context capture
Keeps the reasoning and surrounding context connected to each extracted decision so the record remains meaningful on its own.
Ownership and approval tracking
Captures who proposed, made, or approved a decision wherever that information is stated or reasonably implied by the discussion.
Structured decision log
Compiles findings into a clear, chronological decision log that can be reviewed or exported into a knowledge base or project record.
Three specific decision-extraction problems, three specific fixes.
Decisions phrased informally
Teams rarely say "we have decided" outright, making simple phrase matching unreliable.
Consensus-aware decision detection
We tuned the agent to recognize decisive language and consensus cues across the discussion rather than depending on one explicit decision phrase.
Separating decisions from options discussed
Meetings often explore several alternatives before finally settling on one choice.
Discussion-arc analysis
We had the agent track the discussion arc so it captures the final choice instead of treating every alternative as a decision.
Tentative or conditional decisions
Some decisions are provisional and depend on further input, making them risky to present as final decisions.
Conditional-decision flagging
The agent explicitly flags conditional or tentative language so humans can quickly confirm whether the decision should be treated as final.
"The important distinction was not simply finding decision-like sentences. The agent had to understand the discussion around them so that alternatives, opinions, and tentative calls were not presented as finalized decisions."
From lengthy meeting records to a reliable source of decision truth.
A clear, dispute-free record of what was actually decided in every meeting.
Decisions carry forward reliably into downstream work instead of being forgotten.
Less time spent re-reading transcripts to confirm what was agreed.
A searchable decision history that teams can refer back to over time.
Turn every meeting into a clear, reliable record of decisions.
The Decision Extractor turns meeting transcripts, MOM documents, and discussion notes into a clear, accurate record of what was actually decided. By combining reliable decision detection with context and ownership capture, the agent gives teams a dependable reference point they can act on and revisit. Tentative and conditional decisions are explicitly flagged for human confirmation, making the solution practical for organizations that want their decisions to stick.
Common questions about the Decision Extractor.
Find quick answers to common questions about extracting and managing decisions from meeting records.
What types of meeting records can the Decision Extractor analyze?
The agent can analyze meeting transcripts, MOM documents, and discussion notes, including Word and PDF-based records. The inputs are normalized before analysis so decisions can be extracted consistently across different source formats.
How does it distinguish actual decisions from options and opinions?
The agent evaluates the discussion arc and consensus cues rather than simply searching for phrases such as "we have decided." It separates finalized choices from alternatives that were considered, opinions that were shared, and questions that remained unresolved.
Can the agent capture why a decision was made?
Yes. Each extracted decision is paired with a brief statement of the reasoning or context behind it, allowing the decision record to remain meaningful without requiring the team to revisit the complete meeting transcript.
Does it identify who made or approved a decision?
Where the meeting record states or reasonably implies who proposed, made, or approved a decision, the agent captures that ownership or approval information as part of the structured decision record.
Important decisions getting lost in meeting records?
Our architects can map an AI-powered decision extraction workflow for your meetings, transcripts, MOM repositories, and knowledge systems — starting with a practical deployment, not just a slide deck.
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