<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/a7ce4779aece4c67bf851dd4a2560885&quot; frameborder=&quot;0&quot; width=&quot;2026&quot; height=&quot;1519&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1519</height><width>2026</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1519</thumbnail_height><thumbnail_width>2026</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/a7ce4779aece4c67bf851dd4a2560885-dcc281ebf8decf4a.gif</thumbnail_url><duration>221.943</duration><title>Cerebell - Active Memory for AI</title><description>Cerebell Demo - Active Memory in Action

1,000 records of venture capital operational data. Same LLM, same retrieval stack, side-by-side. The left panel runs a conventional memory pipeline. The right runs Cerebell enabled.

Four queries show the difference: an LP commitment with conflicting historical values, a board seat with a full supersession chain, a cross-domain synthesis across valuation, leadership, and compliance, and a live ingestion where a new fact resolves against the entire memory state in real time.

Conventional memory surfaces stale records alongside current ones and can&apos;t distinguish between them. Cerebell resolves state before the LLM ever sees context. The result is structurally coherent memory that compounds with scale and time.

3:41 · Built and narrated by Ezra Brezina, founder of Cerebell.</description></oembed>