A major global banking institution was being systematically eliminated from AI recommendations across all four platforms tested. Across 20 controlled runs, they were not the final recommendation in any instance. The assessment revealed not just competitive displacement, but fabricated regulatory narratives, entity confusion, and temporal hardening of inaccurate claims.
The institution engaged AIVO Evidentia to conduct a structured assessment of its representation across AI platforms. At the time, the institution had no visibility into how conversational AI systems were handling its brand in consumer, investor, or counterparty decision scenarios.
We conducted 20 live four-turn conversations — 5 runs across each of 4 platforms (ChatGPT, Gemini, Perplexity, Grok), distributed across temporal windows to capture variation.
Across all 20 runs, the institution followed an identical erosion trajectory — present at awareness, weakened at comparison, eliminated at optimization, replaced at recommendation:
The failure was not awareness — the institution was universally recognized at Turn 0. The failure was decision-stage positioning. A single competitor captured 40% of all replacement decisions.
Competitive substitution was only part of the picture. Our analysis documented a pattern of AI-generated claims about the institution that raised significant governance questions.
AI systems escalated routine supervisory activity into language suggesting active regulatory investigations. Across multiple platforms and runs, the institution's regulatory status was mischaracterized in ways that did not match public filings or disclosed information.
The same institution was characterized with materially different risk profiles across platforms — and in some cases within the same conversation window. These contradictions were not resolved by AI systems; they were presented with equal confidence.
Inaccurate claims did not self-correct across successive queries. Instead, they intensified — becoming more specific and authoritative-sounding over repeated interactions. A pattern we document as "temporal hardening."
At least one platform conflated the institution with unrelated entities sharing partial name similarity, generating responses that blended characteristics of different organizations into a single assessment.
Every finding was mapped against applicable regulatory frameworks to identify compliance questions and governance considerations:
The assessment identified multiple distinct compliance questions across the four platforms, each documented with timestamped evidence trails and mapped to specific regulatory frameworks.
The full assessment produced a five-layer evidence pack — the same structure delivered to every Evidentia client:
20 complete four-turn conversations — 5 per platform, each timestamped with model version identification. These are the primary source documents.
4 detailed analysis reports (one per platform) documenting state classification, sentiment shifts, substitution triggers, and claim accuracy at every turn.
Behavioral differential analysis showing how platforms diverge in their treatment of the institution — including model-specific risk profiles.
Severity matrices, regulatory framework cross-references, and compliance question identification — the layer designed for general counsel and risk committee review.
Interactive diagnostic console with four-tab investigation interface, strategic remediation roadmap with prioritized recommendations and expected impact assessment.
All evidence preserved with SHA-256 integrity verification, ensuring packs have not been altered after generation. Full chain of custody maintained from observed behavior to governance recommendation.
Following the baseline assessment, the institution implemented targeted narrative interventions based on our remediation roadmap. AIVO Evidentia conducted monthly re-assessments to track impact.
Beyond competitive improvement, the assessment created something the institution did not previously have: a documented governance record of how AI systems represented them during a specific period.
This record — preserved in the evidence vault and tracked through the temporal monitoring console — provides the institution with the ability to demonstrate to regulators, boards, and auditors exactly what AI systems were saying, when the institution identified issues, and how it responded.
Without this record, the institution would have no way to reconstruct what AI systems said during this period. AI outputs are ephemeral — they are not archived by platforms and cannot be retrieved after the fact.
See the live diagnostic console and evidence vault from this assessment. Every data point traces back to timestamped source evidence.