Forecast the odds, not just the outcome.
Anchored Horizon returns a full posterior over what could happen, not a single number or static range. This matters because teams rarely need “the forecast” in isolation. They need to know whether a target is realistic, how likely they are to hit it, what the downside looks like, and how much confidence the evidence supports. A posterior lets the same forecast answer all of those questions instead of forcing teams to debate a number.
Let comps anchor the forecast.
Comp-based work is useful but brittle: teams cherry-pick analogues or overfit to familiar examples. Anchored Horizon uses comparable cases as a grounded baseline, then lets the target’s own signals move the forecast off it. Strong features pull, weak ones fade, and heavy tails (the small chance of breakout or flop) are modeled, not smoothed away. Comps come from your records, Iridae’s industry priors, or both, versioned and traceable, never cherry-picked.
Confidence scales with real evidence.
Ten near-duplicates should not make the forecast ten times more certain. Anchored Horizon weighs comps by similarity, recency, and outcome quality, then adjusts confidence by how much independent evidence the set contains: wider for thin or repetitive comps, tighter for diverse, well-matched ones. As outcomes resolve, the comp library learns: weights recalibrate, priors sharpen, and tomorrow’s forecast starts from stronger ground than yesterday’s.
Turn goals into evidence thresholds.
Because the forecast is a posterior, Anchored Horizon can work backward from a target: what would need to be true for the team to be 75% confident? This turns forecasting into action. Instead of asking whether a goal “feels achievable,” teams get concrete signals to watch, milestone levels to hit, and early-warning thresholds that say whether the plan is strengthening or weakening.