User-Flagged AI Reliability Index

Make reliability visible.

AI companies publish capability claims. Users live with misses. This index makes output quality legible in public: user-reported flags, visible denominator, top failure reasons, and a review ladder instead of silent drift.

Flag the live pilot Open reviewed cases

0 Public flags

0 Scored outputs

0.00% Flag rate

Zero baseline. First public flags set the benchmark, and the denominator stays visible the whole time.

What the index shows

People already screenshot bad AI. This turns scattered complaints into one denominator-aware scorecard with visible failure reasons and a live running tally.

0Flags recorded

0Unique flagged outputs

0Resolved flags

0Unresolved flags

Review states

This is not a raw accusation wall. The right public shape is a user-flagged reliability index with clear status labels that can mature into reviewed cases.

Reported A user flagged an output and the denominator moved in public.

Reviewed The claim and source path were checked instead of left as a screenshot rumor.

Confirmed The failure mode held up after review and becomes part of product truth.

Retracted A weak or duplicated report gets cleared instead of becoming permanent theater.

Design rules

The point is to make reliability visible, not to turn the internet into an unstructured hate meter.

Show the denominator

Every public flag count must sit next to outputs served. A raw tally alone is dishonest and easy to weaponize.

Let users classify the miss

Bad fit, too vague, and nonsense are different failures. The score should teach builders what broke.

Keep claims reviewable

The public number should be fast. The stronger claim should come later through reviewed cases and explicit status labels.

Projection math

The live flag rate matters because it scales. A small miss rate becomes a large public problem as output volume rises.

At 10k outputs / month

expected public flags at the current observed rate

At 100k outputs / month

expected public flags at the current observed rate

Start where the tally is already live.

NOETRON is piloting this on the Business Name Generator first. Flag the miss. Watch the count move.

Open live pilot See the generator brief Open reviewed cases Open raw API

Top reported reasons

Reason counts are the diagnostic payload. If a model keeps getting reported for the same miss, that is the beginning of product truth, not the end of the review.

0No flags yet

Active producers

Do not pretend the index is broader than it is. These are the live NOETRON surfaces currently feeding the public tally.

No live producers yetWire a public output surface into the accountability rail first.