This is a submission for the Google AI Studio Multimodal Challenge*
What I Built
ClueFrame is an AI-powered, crime-solving applet. Players examine AI-created visual evidence, collect and link clues on an interactive detective board, question leads, and identify suspects while racing against a timer to crack the case.
It solves the “stale mystery” problem by producing endless, believable cases and turns passive puzzles into collaborative, narrative-driven investigations. The applet trains observation, inference, and teamwork in a low-risk, replayable format.
Built for tabletop fans, educators, teams building soft skills, and creators who need rapid scenario prototyping, it makes mystery-solving social and creative. Players don’t just consume a story, they co-author the outcome through evidence and deduction.
How I Used Google AI Studio
I used a two-model multimodal pipeline: gemini-2.5-flash for all text and structured data (scenario generation, clue metadata, refined image prompts) and imagen-4.0-generate-001 solely for photoreal visual evidence.
Gemini produces coherent scenarios, searchable metadata, and on-demand prompt refinements; Imagen renders those prompts into high-fidelity images. The UI links each image to its structured context so images are queryable, annotatable, and filterable on the detective board.
Benefits: photoreal imagery boosts immersion, structured text makes evidence machine-actionable, and rapid text→image refinement lets investigators iterate hypotheses and explore leads in real time.
The code was developed and run in Google AI Studio.
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