Glossary of AI Meeting Intelligence & Meeting-to-Deliverable Work

Acceptance criteria are the specific, testable conditions a feature must meet before a ticket can be considered done. In agile workflows, they are written into Jira or Linear issues so engineers and QA share a definition of “finished.” AI meeting tools like Earmark draft acceptance criteria directly from what was agreed in conversation, so tickets arrive ready-to-dev instead of as one-line placeholders.

An action item is a discrete task assigned to a specific owner with an implied or explicit deadline, produced during a meeting. Traditional notetakers list action items at the bottom of a summary; meeting-to-deliverable tools convert them into the actual work — a drafted ticket, an update post, a follow-up email — so the item is started, not just recorded.

An ADR is a short document that captures a significant technical decision, its context, and its consequences, so future engineers understand why a system is built the way it is. ADRs are one of the highest-leverage artifacts to generate from engineering meetings, because the reasoning behind a decision usually lives only in the conversation that produced it.

An agentic workflow is a process in which AI agents take autonomous, multi-step action toward a goal rather than responding to one prompt at a time. In the meeting context, agentic workflows mean task agents that build summaries, follow-ups, and deliverables while the conversation is still happening — the human sets the intent; the agent does the assembly.

See: Artifact

An AI Chief of Staff is an AI system that operates one step ahead of you across meetings: preparing context before, guiding during, and completing the output after. The metaphor distinguishes tools that assist (“take notes for me”) from tools that operate (“have the update drafted before I’m off the call”). Earmark positions itself as part AI Chief of Staff, part real-time documentation engine, part workflow automation.

An AI meeting assistant is software that uses speech recognition and large language models to capture, transcribe, and act on meeting conversations. The category ranges from simple recorders to real-time systems that generate finished work during the call. The key differentiator is output: a transcript and summary at the low end; PRDs, tickets, and decision logs at the high end.

An AI notetaker is a tool that records meetings and produces transcripts and summaries — examples include Otter.ai, Fireflies.ai, Granola, and Fathom. Notetakers optimize for recall of what was said. Their limitation is that a summary is raw material, not the deliverable: someone still has to turn the notes into the PRD, the tickets, or the stakeholder update. The transcript is raw material, not the product.

Air pockets are gaps in your context — moments where the information you need exists somewhere in past conversations but isn’t retrievable when you need it. Air pockets are why “we discussed this three weeks ago” so often ends in re-litigating the same decision. Meeting memory systems exist to eliminate them.

An artifact is a structured, shippable deliverable generated directly from a live conversation — a PRD, a set of Linear or Jira tickets, a decision log, an executive update, an engineering spec, a follow-up email. Artifacts are not meeting summaries. The distinction matters: a summary describes the meeting; an artifact completes the work the meeting was for. In Earmark, every task produces an artifact, rendered as a card you can refine, share, or push to another tool.

“About the work” work is the administrative layer that surrounds real work: writing up what was discussed, formatting tickets, chasing owners, re-explaining context. It is work that documents work rather than advancing it. Product teams drown in it because meetings historically created this layer instead of completing it.