Assessment · Risk Framework
Narrative Divergence Assessment™
A measurement framework for the gap between your authoritative disclosures and what AI systems are telling investors, regulators, and counterparties about your company.
/ 01 Operator diagnostic
Four questions before we engage.
If you answer no to any of these, an assessment isn't your highest-leverage next move yet. Operators come back to NDA when the answers shift.
- / 01
Have you searched your own company on ChatGPT, Claude, Gemini, and Perplexity in the last 30 days — and saved the answers?
If no — that's the first hour of work, not a paid engagement.
- / 02
Have you compared what those AI systems claim against your actual official disclosures?
If no — you don't yet know whether divergence is the problem.
- / 03
Are at least one of your stakeholder groups — investors, customers, regulators, analysts, counterparties — using AI as their first-pass filter?
If no — the divergence may exist but isn't materially routing decisions yet.
- / 04
Do you have someone on your team who can actually act on findings — legal, IR, comms, or risk?
If no — an assessment without an action owner is shelfware.
/ 02 Definition
What Narrative Divergence™ means.
The measurable gap between (a) your authoritative organizational information — SEC filings, official disclosures, investor communications, controlled statements — and (b) the external AI-generated narratives that increasingly shape how investors, customers, regulators, analysts, and counterparties understand your company.
Five ways divergence shows up:
- InaccurateAI presents claims that contradict your actual disclosures.
- OmittedMaterial context exists in your filings but is missing from AI-mediated answers stakeholders actually read.
- OutdatedAI cites information that's no longer current. Strategy shifts, leadership changes, and resolved matters lag in model knowledge.
- SpeculativeInterpretation gets presented as established fact, with no signal to the user that the framing is inferential.
- FabricatedStatements with no authoritative source — sometimes including invented quotes attributed to executives.
/ 03 What the assessment produces
Four outputs, in order.
/ 01 Identifies
Where AI misreads you.
Across investor, customer, and regulator-facing AI surfaces.
/ 02 Quantifies
How far off the narrative is.
Categorized by severity and stakeholder exposure.
/ 03 Documents
Disclosure-trigger analysis.
Including Reg FD considerations and counsel-routable findings.
/ 04 Routes
Findings to the right owners.
Legal, IR, governance, audit, risk — with clear handoff briefs.
/ 04 Engagement deliverables
What you receive.
- / 01Divergence MapSide-by-side comparison: what your disclosures say vs. what major AI systems say. Sourced and timestamped.
- / 02Risk CategorizationFindings tagged by severity, stakeholder exposure, and reputational / regulatory / financial dimension.
- / 03Disclosure-Trigger MemoReg FD considerations and any disclosure-obligation triggers. Counsel-routable language, not a marketing report.
- / 04Action Brief by OwnerLegal, IR, governance, audit, and risk — each gets a focused handoff, not the whole 80-page report.
- / 05Re-Test ProtocolA repeatable measurement protocol so you can verify whether your remediation work moved the AI narrative.
/ 05 Compliance frame
Built around Reg FD, not around it.
Public companies face the same disclosure-fairness obligations whether information leaks via a trader's text or an AI assistant's hallucination. NDA findings are scoped, sourced, and documented to be counsel-routable — designed for legal review, not as a substitute for it.
The framework is advisory. It does not provide legal, accounting, or regulatory advice. It produces the evidentiary substrate that lets your counsel, auditors, and IR team make the disclosure call.
/ 06 Who engages
Three operator profiles.
Public-Company GC / IR
Reg FD exposure on AI-mediated narratives.
Concerned that AI systems are routing investors and analysts toward selective interpretations of your disclosures. Wants the disclosure-trigger analysis in writing.
PE / Portfolio Risk
AI-narrative exposure across a portfolio.
Multiple operating companies, varying disclosure regimes. Needs categorized findings by company plus a portfolio-level pattern read.
Energy / Industrial Operators
Capital-intensive sectors where narrative drives valuation.
ASX/NYSE-listed energy, infrastructure, and industrial businesses where AI-mediated investor confusion translates directly to cost-of-capital risk.
Engage
Start with the diagnostic.
30-minute scoping call. We confirm whether the four operator-diagnostic answers are yes, frame the engagement, and discuss whether to route via Matthew Bertram's advisory practice or ModalPoint's productized assessment track.
Request scoping call