Select a confidence tier to explore the design decisions
High confidence
75% and above
Borderline
60 – 74%
Low confidence
Below 60%
Example news item
Positive
NVDA beats Q2 earnings forecast by 18%
Reuters · 2h ago
Confidence
92%
Design decisions at this tier
Green bar — full width Perceptual signal that model output is reliable. Analysts can move faster with less cognitive load on verification.
No additional warning per item Adding a warning at 92% would be noise. The advisory banner at the top of the dashboard handles the persistent reminder to verify — it doesn't need repeating on every card.
Ranked higher in feed by default High-confidence signals surface first, respecting analyst time while still exposing lower-tier items below.
Scan headline Read sentiment pill See green bar Add to watchlist Spot-verify source
Example news item
Negative
Supply chain disruptions hit semiconductor stocks
FT · 4h ago
Confidence
65%
Design decisions at this tier
Amber bar — a visual pause Amber is universally understood as "proceed with caution." It doesn't stop the analyst but introduces intentional friction — a moment of hesitation before acting.
Surfaces in "low confidence" filter Items 60–74% appear in the borderline filter view, letting analysts batch their deeper review work rather than context-switching per item.
Hover tooltip: "mixed sentiment language detected" Surfacing the model's uncertainty reason helps analysts know where to focus their verification — not just that confidence is low, but why.
Scan headline See amber bar Read full article Check hover reason Manual classification
Example news item
Negative
Regional bank exposure to CRE raises concerns
Barron's · 6h ago
Confidence
54%
Design decisions at this tier
Red bar — but item stays visible A critical decision: low-confidence items are NOT hidden or suppressed. A 54%-confidence negative signal on a major sector is still analytically relevant. The red bar flags it without removing it.
"Add assessment" action available Analysts can manually reclassify low-confidence items. Corrections feed back into the model as labeled training data, closing the human-in-the-loop.
Batched in a dedicated review queue Low-confidence items queue for a scheduled review session rather than demanding immediate attention, protecting analyst focus during high-volume periods.
See red bar Skip or queue Scheduled review Add assessment Correction logged
Anti-patterns considered and rejected
Hiding low-confidence items entirely — analysts lose visibility into model blind spots
Showing a raw percentage without color encoding — too much mental math per article
Blocking action until analyst verifies — creates bottleneck, destroys efficiency gain