A guide to migrate your meeting notes to Tiro. Upload the zip file you export from Caret and bring speaker labels and timestamps over as they are. Finish exporting your data before September 1, 2026. Migration is available through September 14.

56% of CEOs say they've seen nothing from their AI investment — even after installing every available tool. After breaking down AI Transformation into its five fundamental layers, the reason becomes clear. The biggest gap lies in the very first layer: meetings.

A practical Tiro MCP playbook: turn a weekly KPT retrospective into an automated loop so action items reach Linear before the week starts.

Ontology is the AI buzzword of the season, but there is a layer before it: Pre-Ontology. Without it, no consultant can hand you an ontology that actually fits your organization.

It is no longer the prompt that matters most—it is the context. What context engineering is, how to use it in practice, and the four patterns of context breakdown with fixes for each.

You built every AI tool, but the agent still doesn't know what was decided in the meeting. Here's how to feed that meeting data to an agent with four tools.

How AI agents should interact with your system. A look at the four interfaces: API, MCP, CLI, and Skill.

Most AI transformation stops at the dashboard. The ontology layer is what turns scattered meeting context into decisions an agent can act on.

95% of AI pilots fail to move a revenue number. The pattern isn't about models. It is about a context layer most teams leave empty.

AI is everywhere except the productivity data. Why most AI transformations stall at 10%, and the 5-layer architecture to break through.

Otter.ai does not support Korean. Notta and Granola do. A support matrix built from each vendor's own documentation, plus the per-language accuracy figures Tiro publishes for Korean and Japanese.