Catch the sentences in your 10-Q that AI readers will get backwards
A quarterly report lives on sequential comparisons and guidance language, exactly the phrasing parsers flip. LyraMind's legibility scan finds the spans most likely to be misread and tells you how to rewrite them.
The problem
A 10-Q turns on quarter-over-quarter deltas and forward-looking language, and those are the sentences that carry hedges and polarity flips: 'results were not inconsistent with expectations,' 'partially offset by,' 'no material adverse change.' A quant engine or financial agent can read the opposite polarity and move on before a human intervenes.
How LyraMind does it
Paste the quarter's draft. The legibility scan rates six factors and returns a score out of 100 with a verdict: AI_LEGIBLE, REVIEW_LANGUAGE, or HIGH_MISREAD_RISK. It surfaces the specific flagged spans, the risk each carries (double negative, stacked hedges, hedged sentiment flip, over-long sentence, unquantified claim), and a plain fix.
What you get
A ranked list of the riskiest sentences with rewrites: state it in the positive, commit to a figure, split a hedged flip into two sentences. You also get the factor breakdown so you can see whether the whole quarter reads cleanly or one section drags it down. The demo works on any text you paste, including your own 10-Q draft.
- Paste the 10-Q draft or a specific section
- Verdict: AI_LEGIBLE, REVIEW_LANGUAGE, or HIGH_MISREAD_RISK
- Flagged spans ranked by misread risk
- Each span gets a concrete rewrite
- Factor breakdown across hedging, quantification, negation, structure
- Escalate the same text to the full AI Readiness Report
Questions
How is this different from the AI Readiness Report?
The scan is the fast legibility primitive: a score, factors, and span-level fixes. The report adds the synthetic institutional panel and predicted questions on top of the same scan.
Will it check my numbers for accuracy?
No. It is a legibility and machine-readability analysis of the language, not a factual or accounting audit. It flags claims that are hard to extract or ambiguous, not claims that are wrong.
Can I run it repeatedly on edits?
Yes. It is deterministic and offline-safe, so you can iterate on wording and re-scan until the risky spans clear.