What is Context Cruncher?
Context Cruncher transforms casual voice recordings into clean, structured context data that AI systems can use for more personalized results. Using Gemini AI's multimodal capabilities, it processes audio directly - understanding, cleaning, and organizing your spoken words into useful context data.
Below is a real example using a voice recording about movie preferences. Notice how the raw, conversational audio has been transformed into organized, third-person context data ready for AI applications.
Ready to try it yourself?
Process your own audio and create structured context data
Launch Context CruncherDemo Results
This demo processes the example audio file (movie-prefs.opus) included in the repository.
The audio contains casual thoughts about entertainment preferences, and Context Cruncher has
extracted structured context data from it.
Markdown Output
Human-ReadableUser's Entertainment Preferences - Israel-Based, Tired Parent
Entertainment Preferences
General
- the user prefers to watch content that is either based on a true story or is credible
- the user is not a fan of science fiction
- the user enjoys content with intriguing stories
- the user occasionally enjoys horror movies
- the user likes thoughtful content
- the user is not a fan of alarmist commentary about technology, but is interested in themes surrounding the parameters of privacy
- the user prefers movies that take their time rather than overload with special effects and violence
- the user does not enjoy World War movies or historical movies that depict wars, battles, and conquests
- the user likes comedy movies, but not rom-coms
- the user likes obscure travel content
- the user is interested in technology, artificial intelligence, cybersecurity, politics, hacking, surveillance, and intelligence
Formats and Platforms
- the user enjoys Netflix documentary series, especially offbeat documentaries
- the user appreciates the content produced by Vice
Specific Movies and Genres
- the user enjoys the genre of the absurd (e.g., Waiting for Godot)
- the user liked the movies "Get Out", "The Matrix", "That Guy" (potentially "Vanilla Sky"), and "Limitless"
- the user is interested in movies that explore the question of reality, such as "Inception"
Content Preferences Related to Israel
- the user lives in Israel and follows geopolitical content
- the user prefers content that explores conflict with less emphasis on the military aspect, focusing instead on personal narratives and interpersonal connections across divides
- the user finds shows like "Fauda" too real and conflict-heavy
- the user is a new parent and tired, making it difficult to navigate the geo-specific content restrictions imposed by streaming providers in Israel
Recommendation Preferences
- the user is interested in recommendations of recently released content
- the user wants recommendations that take into account the user's geographic location in Israel, and exclude content that is difficult to access there
Captured on: 2025-10-26 21:25:43
JSON Output
Machine-ReadableKey Features Demonstrated
Intelligent Cleaning
Removes filler words, tangents, and irrelevant information while preserving all meaningful context.
Structured Organization
Automatically organizes information into logical categories and hierarchies.
Third-Person Conversion
Transforms first-person narratives into third-person context data about "the user".
Multiple Formats
Outputs both human-readable Markdown and machine-readable JSON formats.