A statistic that doesn’t survive a statistician’s Twitter thread. A source whose conflict of interest wasn’t surfaced. An investigative claim whose primary document has an alternative interpretation the reporter didn’t consider. These are the corrections that define a publication’s credibility. Augle runs the adversarial review before publication — so corrections become optional rather than inevitable.
Most journalism corrections are not about the central claim — they’re about a supporting statistic that was misrepresented, a source whose independence wasn’t adequately verified, or a research finding that was drawn from a press release rather than the actual study. These are the errors that don’t change the story but define how the story is remembered. Augle catches them before publication.
Questions journalism teams run on Augle
Editorial integrity mode monitors statistical claim accuracy throughout the session. Relative risk presented without an absolute baseline, a correlation stated as causation, a subgroup finding presented as a main effect, or a percentage change calculated from an unrepresentative base — each is identified before it enters the published article. These are the statistics that statisticians correct on social media the morning after publication.
The Guardian’s SVS distinguishes press releases from peer-reviewed publications and checks the original source against the claim being made. A press release that overstates the study’s findings, omits the sample size limitation, or drops the confidence interval — and that the reporter has relied on without reading the original — is flagged. The paper says something different from the press office.
Editorial integrity mode monitors source independence throughout the deliberation. A quoted expert who has received funding from an industry the story concerns, a think tank whose positions are funded by a party with a direct interest in the story’s framing, or a ‘independent’ report that was commissioned by the subject of the investigation — these are flagged for editorial consideration before publication.
The Contrarian takes the role of the most credible critic of your story’s central claim — surfacing the alternative explanation for the evidence, the methodological objection to the central thesis, the data that cuts against the narrative. A story that has been tested against its strongest counter-argument, and has an answer for it, is a story that holds up. One that hasn’t been tested doesn’t know whether it holds.
Each scenario illustrates realistic deliberation behaviour across investigative journalism, science reporting, and data journalism.
“Does the evidence in this investigation into a major housebuilder’s planning application practices support the central claim, and what is the strongest counter-argument the subject’s legal team will advance?”
The alternative explanation was the legal team’s entire pre-publication letter. The reporter had considered it but the article didn’t address it directly. A reframed statistical claim with the alternative explanation presented and addressed turned a legally vulnerable story into a defensible one — without changing the central finding.
“Does our draft story on a new study linking ultra-processed food consumption to cognitive decline accurately represent what the study actually found, or are we overstating the findings?”
The press release was accurate about the relative risk — it just omitted the absolute baseline, the population scope caveat, and the causal language limitation. Four targeted edits turned a story that would have generated a correction from the study’s authors into one they could endorse.
“Does our analysis showing that housing affordability has deteriorated faster in regional cities than in London hold up to scrutiny, and what is the most likely methodological challenge?”
The ONS lower quartile analysis took two hours. It confirmed the regional vs London pattern — the finding was robust under the more rigorous measure. The story was published with both measures cited, which pre-empted the methodological objection and made the analysis stronger, not weaker. The alternative was publishing a median-to-median analysis and receiving a correction request from a housing economist.
Upload the draft article, supporting research papers, data analyses, and source documents. Editorial integrity mode activates — the Guardian checks source independence, distinguishes press releases from peer-reviewed papers, monitors statistical claim accuracy, and flags original source vs. secondary report discrepancies before any agent receives the evidence.
The Cartographer classifies every claim as Settled, Contested, or Unknown based on the available evidence. The Methodologist evaluates whether the evidence supports the conclusions drawn — whether causal language is justified by the study design, whether the population scope applies to the story’s framing, whether statistical representations are accurate relative to the original source.
The Contrarian takes the role of the story’s most credible critic — a subject’s legal team, a statistician on social media, a rival journalist, or a hostile expert. Every objection specifies a resolution condition. Unresolved Strong objections appear verbatim. These become the pre-publication edit list, not the post-publication correction notice.
The Synthesizer produces a finding anchored to the evidence — not the story’s thesis. Confidence grades per claim, unresolved objections verbatim, and reopen conditions. The audit trail is exportable — for legal pre-publication review, editorial governance, or the public interest defence records that regulated journalism requires.
Editorial integrity mode monitors every statistical claim against the original source — relative risk without absolute baseline, correlation framed as causation, subgroup findings presented as primary results, percentage changes from unrepresentative bases. These are the errors that generate corrections from academic statisticians within 24 hours of publication. Catching them before publication is the entire value.
The Contrarian surfaces the most credible objection to your story’s central claim — the alternative explanation a legal team will advance, the methodological objection a rival journalist will raise, the conflict of interest a reader will find. Unresolved objections appear verbatim with resolution conditions. A story that has been tested against its strongest counter-argument is a story that holds up under scrutiny.
Every session produces an exportable record — SVS verification outcomes, confidence grades per claim, every objection raised and its resolution status. For regulated journalism, high-stakes investigations, and pre-publication legal review, this record demonstrates that the evidence base was reviewed to the standard that the story’s claims require. It’s also the record that matters if a correction is later disputed.
Join the waitlist and run a session on your next investigation before it goes to publication.