Make compensation and governance disclosure legible before you file
A proxy statement is dense with compensation tables, governance narrative, and say-on-pay rationale, all of which get machine-read ahead of the vote. LyraMind scores the DEF 14A for legibility and surfaces the questions a regulatory and institutional reader will ask.
The problem
Proxy narrative around pay-for-performance and governance leans on qualifying language, and the compensation discussion mixes quantified tables with prose rationale. Institutional stewardship teams and their tools read it closely ahead of say-on-pay, and a hedged or hard-to-extract rationale can read as a governance flag it was never meant to be.
How LyraMind does it
Paste the compensation discussion and governance sections. The AI Readiness Report scores legibility and runs the synthetic panel, where the regulatory reader (weighting negation clarity and quantification) and the credit and active-manager archetypes each parse the rationale. It returns consensus comprehension, the blind spots, and the questions the proxy will trigger.
What you get
A readiness readout on the proxy, flagged spans in the pay and governance narrative with rewrites, the regulatory-reader blind spots, and the predicted questions to pre-empt before the vote. The demo runs on any text you paste, so your DEF 14A draft works the same way.
- Paste the compensation discussion and governance sections
- Legibility scored with a regulatory-readiness dimension
- Synthetic regulatory and institutional reader blind spots
- Predicted questions the proxy will raise before the vote
- Flagged pay-rationale spans with rewrites
- Overall verdict: AI_READY, IMPROVE_BEFORE_FILING, HIGH_MISREAD_RISK
Questions
Does it evaluate our governance practices?
No. It evaluates how legibly the disclosure reads, not the substance of your compensation or governance. It is not governance, legal, or investment advice.
Which dimension matters most for a proxy?
Regulatory readiness, which reflects whether obligations and rationale read unambiguously. The scan weights negation clarity and quantification for exactly that.
Can it help with say-on-pay preparation?
Indirectly. The predicted questions and regulatory-reader blind spots tell you where the pay narrative invites follow-ups, so you can tighten it before the vote.