Intelligence Models
Repute AI processes public Telegram data through five specialized intelligence models. Each model targets a distinct structural dimension. Together, they compose into the REP score.
The Five Models
| Model | Full Name | What It Measures | Output Range | Feeds Into REP |
|---|---|---|---|---|
| AVI | Attention Velocity Index | Acceleration of attention over time | Unbounded (normalized) | Yes |
| SIS | Structural Integrity Score | Authenticity of Telegram community | 0–100 | Yes |
| NSM | Narrative Strength Model | Strength and compounding power of narrative | Unbounded (normalized) | Yes |
| BPM | Breakout Probability Model | Probability of structural breakout | 0–1 | Yes |
| TRI | Telegram Risk Index | Bot risk + decay + fragmentation (inverted) | 0–100 | Yes (as penalty) |
How They Combine Into REP
REP is a weighted composite of the five model outputs, scaled to a 0–1000 range. TRI acts as an inverse input — higher TRI reduces REP.
The general composition formula:
REP = scale(
w_AVI × AVI_normalized +
w_SIS × SIS_normalized +
w_NSM × NSM_normalized +
w_BPM × BPM_normalized −
w_TRI × TRI_normalized
)Where scale() maps the weighted sum to the [0, 1000] range using a monotonic transformation, and weights reflect the relative importance of each structural dimension.
Model weights are not disclosed in full to prevent gaming. The relative ordering of importance is: SIS > AVI ≈ NSM > BPM > TRI (inverted).
Model Independence
Each model is designed to measure a structurally independent dimension. A community can have:
- High AVI (rapid acceleration) but low SIS (bot-driven acceleration)
- High SIS (authentic community) but low NSM (narrative not spreading)
- High BPM (breakout-ready structure) but high TRI (bot risk undermining it)
This independence means composite REP cannot be gamed by optimizing a single dimension. All five must cohere.