Riveln

The pipeline

Five stations, one machine.

Agents read the open field continuously: threads, posts, comments, videos.

Posts, threads, comments, videos

The model reads every candidate mention and decides what it is: which brand, which niche, real talk or noise.

Brand, niche, context

Mentions dedupe and merge into one record per brand. One name, one record, however loud the field gets.

One brand, one record

Each brand gets a momentum score from 0 to 100, normalized within its niche and smoothed over time.

0 to 100, niche normalized, smoothed

Scores become a daily brief, dossiers on demand, and spike alerts, on the desk and in Telegram.

Brief, dossier, alert

The number

0 to 100, recomputed continuously. The scale never moves. The reads move across it.

Figure a. One number per brand on a fixed scale, recomputed continuously. The notch is the read today, with earlier reads fading behind it.

A niche of quiet forums and a niche of loud video are graded on their own curves.

Figure b. Each niche is graded on its own curve, so a standout in a quiet niche is not buried under a louder one.

Momentum is a trajectory, not a single spike. One viral day cannot whipsaw the number.

Figure c. We publish the smoothed trajectory, not the daily noise, so a single loud day does not swing a brand's read.

Scale recomputed continuously. Reads as of 2026-06-12.

Four reads

Discipline

Jurisdiction

Built and operated in the European Union.

Residency

Signal data is processed and stored on EU infrastructure.

The read

Read continuously, niche normalized, evidence linked

The same engine runs behind every Riveln desk. Open Prodrom to watch it read a board live, or ask us for a desk of your own.