Real video frame analysis
Extract frames from video and analyze them with Vision LLMs — not just text summaries of existing metadata.
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Technology & capabilities
Technical capabilities now working in VisionaryAI Suite — diagnostics, fusion, local Vision models and operational limits.
Technical capabilities — now working
Multimodal payloads
Send actual image frames in OpenAI-compatible vision payloads alongside speech, OCR and context signals.
Timeline-aligned scenes
Connect visual understanding to precise timeline events — searchable multimodal moments across your library.
Grounded cinematic descriptions
Produce scene descriptions tied to frame evidence — composition, action, atmosphere and on-screen detail.
Hallucination control
Evidence-based fusion separates grounded observations from interpretation and flags uncertain assumptions.
Vision diagnostics
Confidence scoring, grounding scores and evidence sources — inspect how conclusions were reached.
Multi-signal fusion
BLIP, CLIP, OCR, speech, metadata and Vision LLM output combined into coherent timeline intelligence.
Local-first via LM Studio
Run Gemma Vision and other supported models locally through LM Studio — private media stays on your machine.
Searchable timeline events
Multimodal events indexed in Semantic Memory — find moments by what was seen, said or read on screen.
Vision diagnostics
Benchmark and latency tooling in Trial 1.5.2 complements Vision diagnostics: measure pipelines on real media, compare baselines and export reports. Vision-specific diagnostics expose confidence, grounding and evidence per event.
Local-first via LM Studio
Gemma Vision and other supported models run through LM Studio on your hardware. See the LM Studio setup guide after approval.
Vision Intelligence is an early operational breakthrough — results vary by model, hardware and media type. VisionaryAI Suite falls back to text-based analysis when vision payloads are unavailable.