GoldenRetriever

What is GoldenRetriever?

GoldenRetriever is an AI-powered search tool that indexes a user’s media library—videos, audio, and other files—so you can ask questions in plain English and get answers grounded in the original content. It aims to search by meaning and visual/audio context, not only by text transcripts.

The core purpose is to help people find specific moments, decisions, or information inside recordings, presentations, and documents that are typically hard to search with conventional keyword search. The product is offered as a public beta for macOS.

Key Features

• Multimodal understanding of original media (video and audio) so search can rely on what’s seen and heard, not only what’s transcribed.
• Visual context processing for slide decks and on-screen presentations, including demos and whiteboard-style sessions, where diagrams and what’s displayed may matter.
• Audio-focused indexing that uses the full audio signal (not just transcript text), intended to preserve emphasis and context.
• Broad file indexing beyond media: PDFs, Word documents, PowerPoints, images, and plain text—so media and documents can be queried together.
• Question answering with sources and timestamps, and references that include where relevant within the media (e.g., timestamps and slide numbers).
• Local file control positioning: the page states that your files “never leave your control,” indicating the indexing/search is designed to respect user control.

How to Use GoldenRetriever

1. Download the macOS public beta and set it up so it can index your files.
2. Add sources to index, including your Mac, external drives, and shared volumes (as described on the site).
3. Run indexing for the file types you want to search (videos, audio, PDFs, documents, slides, and images).
4. Ask questions in plain English, then review sourced answers that include timestamps (and slide references when applicable).

Use Cases

• Locate a specific moment in long-form video archives: e.g., a wedding videographer searching for “every wedding kiss shot from the last three years.”
• Find details that may not appear in transcripts: e.g., a filmmaker/DIT searching for “the shot of the red car at golden hour,” where visual context matters.
• Retrieve internal documentation hidden inside decks and recordings: e.g., an engineering lead asking “What did we decide about the auth migration in the eng sync three weeks ago?”
• Support qualitative research and synthesis by anchoring to the non-text parts of sessions: e.g., a UX researcher or research team seeking the moment relevant to an interview or conversation.
• Cross-reference legal, academic, or operational information across many documents: e.g., a lawyer searching for “the clause about indemnification across 200 PDFs,” or an academic searching for where a paper mentions a specific effect size.

FAQ

• Is GoldenRetriever a transcription-only search tool? No. The product page emphasizes that GoldenRetriever “doesn’t just read the transcript” and uses multimodal AI to understand visual and audio context.

• What file types can be indexed? The page lists videos, audio, PDFs, Word documents, PowerPoints, images, and plain text, along with “slides” and screenshot/scan-like inputs.

• Where can files be indexed from? It states GoldenRetriever can index your Mac, external drives, and shared volumes.

• Does GoldenRetriever provide evidence for answers? Yes. Answers are described as sourced with timestamps (and slide numbers referenced in relevant scenarios).

• What platforms are supported? The product is presented as a public beta for macOS on the site.

Alternatives

• Transcript-based video search: Tools that convert video to text and search within transcripts. These can be useful when the key information is fully captured in words, but may miss meaning carried by visuals or audio emphasis.
• Local knowledge-base search tools: Document search applications that index PDFs and text-based files. They can cover written materials well, but typically don’t search video/audio by visual or audio context.
• Media management platforms with tagging/metadata: Systems that rely on manual tagging or extracted metadata. They can help organizing large libraries, but generally require more setup and may not answer questions about specific moments.
• General-purpose AI chat with retrieval over documents: Chat interfaces that retrieve relevant snippets from an indexed corpus. Depending on the underlying indexing, they may focus on text extraction rather than multimodal understanding of the original media.