NeuroVidz

Most video analytics tools count views and guess at retention curves. NeuroVidz goes a step further: you upload a clip — mp4, mov, or webm, up to three minutes — and the system extracts 32 perceptual signals per second, mapping them to modeled brain response across attention, engagement, and emotion. The result is a 0–100 engagement score that moves with what is actually happening on screen and in the audio track, plus a precise drop-off timestamp and a specific edit recommendation for that moment.

The vendor’s core claim is that other tools are, in their words, ‘half-deaf’ — they read frames and ignore sound. NeuroVidz feeds voice energy, rhythm, music, and silence into the same read as the visual signals. This makes the tool materially different for creators whose content is voice-driven: podcasters who post video cuts, talking-head educators, or music-adjacent clips where a beat drop or a pause before a punchline carries real engagement weight.

Where it fits well: an editor reviewing three or four takes of a short-form clip who wants a quantitative second-by-second read faster than manual scrubbing. Where it breaks: the three-minute clip ceiling rules out long-form content entirely, and the credit-based model means high-volume workflows — comparing ten takes in a single session — will exhaust a free allocation quickly. No API is available, so the output stays inside the NeuroVidz interface and cannot be piped into an existing editorial pipeline or reporting dashboard. Teams needing that integration will reach for a different tool.

Sign-in is passwordless via Google or email link. The founding cohort receives 40 credits to analyze initial clips. There is no self-hosted option and no described export or API surface, so all processing happens on the vendor’s infrastructure.