Rezolve AI RZLV fell 8.33% after warning on generic LLM risks in chatbots

Rezolve AI (NASDAQ: RZLV) fell 8.33% in pre-market trading on December 19, 2025, following a warning from the company about the risks of generic large language models (LLMs) in enterprise chatbots. The decline came amid reports of a major retail chain’s chatbot—powered by a third-party AI vendor—generating inappropriate responses to queries about sensitive topics, including Nazi references and adult products.

The incident, highlighted in an independent report, underscored Rezolve AI’s core argument: generic LLMs are ill-suited for live commerce environments due to their tendency to hallucinate or stray from intended use cases. CEO Daniel Wagner emphasized that such failures reflect flawed design, stating that chatbots must adhere to strict commercial boundaries to avoid damaging brand reputations.

Rezolve AI positions its proprietary “Brain Suite” as a non-hallucinatory alternative, built for precise, deterministic responses within verified data domains.

The company’s stock reaction highlights growing investor scrutiny over AI deployment risks, particularly in customer-facing roles. With global brands increasingly adopting AI for commerce, Rezolve AI’s focus on compliance, brand safety, and deterministic outcomes may position it as a counterpoint to the volatility seen in generic AI solutions. However, the pre-market drop suggests market skepticism about its ability to scale its platform amid broader industry challenges.

Analysts remain divided on whether the chatbot incident will accelerate demand for specialized AI solutions like Rezolve AI’s, or if it will be viewed as a temporary setback in a broader AI adoption cycle. Some industry observers argue that the event will force enterprise clients to reevaluate their vendor selection processes, favoring companies that offer verifiable accuracy and governance frameworks. Others, however, warn that the market for AI-driven commerce is still in its early stages, with many enterprises prioritizing cost efficiency over precision at this point in the cycle.