{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/e27e08ccc2ad4a84a124b9d2ffaf36f8\" frameborder=\"0\" width=\"1406\" height=\"1054\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1054,"width":1406,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1054,"thumbnail_width":1406,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/e27e08ccc2ad4a84a124b9d2ffaf36f8-5ce78c2d3c5df037.gif","duration":218.688,"title":"Mira &amp; AI Assitance","description":"This Loom explains how Mira Studio helps teams get faster, macro-level insights using natural language prompts and dashboard-ready outputs. It covers the prompt library templates for areas like analysis and brand monitoring, and introduces scheduled prompts for recurring analyses. The speaker emphasizes grounding a Mira project with detailed context such as DIY versus professional contractor segments, product lines under each, industry trends, regulatory bodies, and key competitors so extracted insights can be attributed correctly. Using a Pro versus DIY example for the last 30 days on Reddit, Mira identifies major gaps in theme, sentiment, and volume and cites specific Reddit threads where those narratives appear."}