Make sure the AI readers get your message right
Your IR message is increasingly consumed by AI systems that summarize and re-tell it. LyraMind tests whether that machine reading matches your intent, flags the language that will be misparsed, and gives you a grounded issuer surface you can embed.
The problem: machines re-tell your message, and can garble it
A press release, an earnings script, or an 8-K narrative is written for people, but AI systems now parse it and re-tell it downstream. Hedged phrasing, buried caveats, and ambiguous structure can invert your intended message by the time it reaches a reader through a model.
Comms and IR have no dashboard for how legible a message is to those readers. You find out after it has already been summarized and circulated.
What LyraMind gives you: a legibility check and a grounded issuer surface
The disclosure scan returns a verdict (AI_LEGIBLE, REVIEW_LANGUAGE, HIGH_MISREAD_RISK) with the specific spans that drive misread risk and a plain remediation for each, so you can tighten language before release. The AI Readiness Report and simulator show how institutional, retail, and regulatory readers would each parse the draft and what questions it will trigger.
For the always-on side, the white-label issuer explainer and shareholder Q&A are grounded strictly in citable market data: the Q&A refuses to answer rather than guess, and every answer cites what it is built from via a compliance ledger. You can embed it under your own brand on your IR site with copy-paste iframe or script code.
Honest scope: this is machine-legibility and editorial analysis of your public text, never advice or a prediction, and the demo runs on sample data.
How you use it
Run every material message through the scan during drafting, use the simulator to pressure-test how different reader lenses receive it, and deploy the embeddable issuer explainer and Q&A on your IR site so shareholders get grounded, cited answers instead of a chatbot that guesses.
Every output records to the Trust Ledger, giving comms and legal an audit trail for the analysis.
- Span-level legibility flags on press releases, scripts, and filings with remediations
- Reader-lens simulation: institutional, retail, and regulatory parses of your draft
- Predicted questions your message will trigger
- White-label, embeddable issuer explainer for your IR site (iframe or script)
- Grounded shareholder Q&A that cites its sources and refuses rather than guesses
- Compliance ledger and Trust Ledger for an auditable record
Questions
What does the embeddable Q&A do when it does not know?
It refuses rather than guesses. Shareholder answers are grounded only in data the platform can cite, and every answer records what it is built from in a compliance ledger.
Is this SEO for AI, or something else?
It is machine-legibility analysis of your public message: does the AI reading match your intent. It does not claim to game rankings inside any proprietary model, and it never predicts a named model's output.
Can we run this before a release goes out?
Yes. The scan and readiness report are pre-publication tools: paste the draft, get the verdict and flagged spans, tighten the language, then release. The core scan is deterministic and offline-safe.