Noodle4 Labs
The Engineering of Market Truth — why social listening data is noisy, and the pipelines we built instead.
Social listening is noisy on video because most tools match brands on ASR transcripts. General speech models are not trained on beauty names, so Rhode becomes Road and silent pack-shots vanish.
Most social listening tools do not read every comment. Frontier LLMs are too expensive at thread scale, so they sample. The allergic reactions and product hacks live in the outliers that sampling throws away.
In a five-brand haul, “this made me look splotchy” is not negative for every product on screen. Isolated comment scoring cannot tell which SKU the viewer meant.
Sending one frame per second to a frontier vision model is roughly $200 a month per heavy user. Most of those frames cannot answer the question you are asking, so the cost is unscalable as a product.
Open, single-shot VQA changes its answer every time you ask. Without a fixed schema the model narrates, invents products, and cannot be rolled up. A harness that demands mechanical evidence is what made outputs stable.
Typical video summaries are 80–90 percent filler. If you search those summaries, the model is reading noise and forgetting the hook, friction, SKU, and claim that actually change a brief.
Logo detection is not enough for influencer compliance. Multi-SKU brands need to know which product was used, and whether a competitor appeared in frame — including in silent video.
High reach is not high conversion. A morning routine with 15 million views can carry about 1 percent purchase intent, while a 10k-view wear-test sits near 35 percent. Rank on hook × comment intent, not likes.