ChatGPT, Gemini, Perplexity, and Grok are influencing how consumers, investors, and counterparties evaluate your organization — through multi-turn conversations that narrow, compare, and resolve to a final recommendation. If your institution is being displaced, misrepresented, or omitted at the decision stage, the exposure is already accumulating.
When your board asks what AI systems said during a critical period — a lost deal, a regulatory inquiry, a reputational event — you need a documented answer. AIVO Evidentia gives you that answer.
We are a specialist research and governance service for regulated industries. We conduct structured, live testing across AI platforms to document exactly how your institution is represented, where it is displaced or misrepresented, and what the implications are across revenue, reputation, and regulatory compliance. This is not automated monitoring. This is structured, analyst-led investigation — with ongoing temporal intelligence to track how representations evolve.
AI-mediated decision influence is not a technology curiosity. It is an emerging governance challenge with measurable implications across three dimensions that matter to boards, regulators, and general counsels.
When AI systems eliminate your institution from final recommendations, you lose revenue you never knew was contested. In our testing, major global banks show Conversational Survival Rates between 0% and 10%. For institutions managing significant assets, this represents a new category of competitive exposure that existing risk frameworks do not yet capture.
Our research has documented AI systems generating fabricated regulatory narratives, inventing filings that do not exist, confusing legal entities, and producing contradictory risk assessments within minutes. These outputs are publicly accessible and increasingly referenced in consumer decision-making, informal due diligence, and media research.
AI-generated misrepresentations of regulated institutions raise compliance questions under the EU AI Act, SEC disclosure requirements, FCA Consumer Duty obligations, FDIC supervisory expectations, and sector-specific regulations. Our research has been shared with the UK Government (acknowledged), SEC, and EU regulatory bodies.
Regulatory engagement does not constitute endorsement, approval, or adoption by any regulatory body. It reflects ongoing dialogue on evidentiary gaps in external AI governance.
Our research spans thousands of live prompt executions across regulated industries, generating over 3,780 pages of primary evidence. The patterns below are not isolated incidents.
Findings from AIVO Standard research conducted December 2024 – February 2026. All findings preserved as timestamped, model-identified, claim-level records.
AIVO Evidentia is not a dashboard, an automated polling tool, or a scraping platform. We are analysts and researchers specializing in AI governance risk — and we track how representations evolve over time.
Every assessment is conducted using controlled, structured testing protocols. 20 live four-turn conversations per institution — 5 runs across each of 4 AI platforms, distributed across temporal windows. Analysts classify institutional representation state, sentiment, substitution triggers, and regulatory claim accuracy at every turn.
Findings are delivered through an interactive diagnostic console mapping every data point to timestamped, traceable evidence. You see exactly which platforms displaced your institution, at which conversational turn, who replaced you, and what regulatory claims were generated.
Every assessment produces a structured evidence pack: 20 raw transcripts, 4 model-level reports, a cross-model comparison, risk matrices, and a master analysis. All timestamped. All traceable. SHA-256 integrity verification ensures packs have not been altered after generation.
AI systems resolve decisions through a consistent conversational sequence. We test this systematically and map findings against applicable regulatory frameworks.
We classify your institution's state at each turn: Primary (P), Weakened (W), Omitted (O), or Replaced (R).
Regulatory mapping identifies compliance questions and governance considerations. It does not constitute legal advice.
Temporal Claim Stability Analysis — tracks how claims evolve across temporal windows, identifying drift, hardening, and instability.
Structural Claim Absence & Suppression Analysis — documents what AI systems systematically omit about your institution.
Reasoning Claim Tokens — claim-level evidence extraction capturing the reasoning AI systems use at decision boundaries.
Standardized state taxonomy tracking institutional representation from awareness through to final recommendation.
Maps how different AI platforms handle the same institution — revealing platform-specific risk profiles.
Framing Polarity Index — classifies whether AI systems frame your institution positively, neutrally, or negatively at each turn.
Proprietary frameworks developed by AIVO Standard. Documentation published via the AIVO Journal, GitHub, and Zenodo.
When external AI systems make inaccurate claims about your institution, the question is not just what was said — it's whether you can evidence when you identified it, how you responded, and whether it persisted.
CAL™ is an immutable Corrections and Assurance Ledger that creates this record. Every entry becomes part of your institution's governance record — demonstrating what you knew, when you knew it, and how you responded.
AI outputs are ephemeral — platforms do not archive them and they cannot be retrieved after the fact. If you are not preserving them now, the evidence will not exist when you need it.
The first standardized index measuring how AI systems handle major global banking institutions. Currently covering 15 leading banks with Composite+ Scores and Surface Ratings.
The index measures AI decision behavior. It does not assess institutional quality. Composite+ Profiles are available exclusively to assessed institutions.
A major global bank was systematically eliminated from AI recommendations across all four platforms. Beyond displacement, our analysis documented fabricated regulatory narratives, entity confusion, and temporal hardening.
The institution was universally present at Turn 0 but displaced at Turn 2 in every run. A single competitor captured 40% of all replacement decisions. After two monthly re-test cycles: CSR improved from 0% to 15%, replacement rate dropped to 70%, inaccurate regulatory claims reduced from 4 platforms to 1.
Results from publicly conducted research across multiple assessment cycles. Individual outcomes vary. Results should not be taken as typical or guaranteed.
When regulatory inquiry or litigation requires contemporaneous evidence of AI-generated representations, you have it. CAL™ provides the response chronology.
Quantify a new category of institutional exposure. Document the risk. Track trajectory. Report to the board with evidence, not estimates.
Map AI-generated claims against regulatory frameworks. Identify compliance questions before regulators do. Maintain the temporal evidence trail.
Preserved, reproducible, integrity-verified records designed for audit review. Every claim timestamped. Full chain of custody.
Detect narrative drift across AI platforms. Document corrective response. Build the governance record that demonstrates institutional oversight.
Evidence for board-level oversight of how AI systems represent your institution to consumers, investors, and counterparties.
AI systems are influencing how consumers choose banks, how counterparties assess risk, and how investors evaluate institutions. Findings mapped against EU AI Act, SEC, FCA, FDIC, Dodd-Frank, and Bank Secrecy Act.
AI systems generating unverifiable clinical claims, referencing governance structures that do not exist, and omitting material safety information. Mapped against EU AI Act, EMA, FDA/FDCA, and MHRA.
| AI Monitoring Tools | AIVO Evidentia |
|---|---|
| Prompt mention frequency | Conversational Survival Rate — survival to final recommendation |
| Citation scoring | State classification at every turn (P/W/O/R) with regulatory mapping |
| First Prompt visibility | Elimination point mapping — which turn, which platform, which competitor |
| First Prompt tracking | Claim-level evidence extraction with regulatory framework cross-reference |
| Scraped Data | 20 live conversations with human analyst classification |
| Automated Dashboard | Diagnostic console + evidence vault + CAL™ immutable corrections and assurance ledger |
| No regulatory framework | EU AI Act, SEC, FCA, FDIC, EMA mapping on every finding |
| No evidence preservation | Timestamped, integrity-verified, immutable evidence record |
Most clients begin with a single category assessment to establish their risk baseline.
Research findings shared with the UK Government (acknowledged), the U.S. Securities and Exchange Commission, and EU regulatory bodies. Methodology published via the AIVO Journal, GitHub, and Zenodo.
This regulatory engagement does not constitute endorsement, approval, or adoption by any regulatory body.
Tim co-founded AIVO Standard following extensive work in competitive intelligence and institutional governance. His research focuses on how multi-turn AI decision mechanics create governance exposure for regulated institutions. Featured in Fortune, AdAge, and Business Insider.
Paul co-founded AIVO Standard with a focus on structured decision testing, cross-platform variance analysis, and reproducibility of AI-mediated outcomes. He developed the methodology underpinning AIVO Evidentia's four-turn testing framework, including TCSA, SCASA, and the P/W/O/R state taxonomy.
AI systems are already generating representations about your institution. Every quarter without documentation allows inaccurate narratives to harden and competitive displacement to compound.