New York – July 7, 2026 – Rezolve Ai (NASDAQ: RZLV), a global leader in AI-powered commerce and engagement, today announced Auditable AI, a breakthrough technology designed to bring unprecedented transparency to artificial intelligence by enabling every AI-generated product recommendation to be explained, verified and understood.
Developed by Rezolve Ai Labs (RAILS), Auditable AI represents the latest advancement in Rezolve’s proprietary Brain Suite platform and addresses what many regard as one of the final barriers to widespread enterprise adoption of AI: trust.
While today’s AI models can generate remarkably sophisticated recommendations, they typically provide little visibility into how those recommendations were reached. Auditable AI changes that by generating a clear, human-readable explanation for every recommendation, grounded in verified customer preferences, product attributes, purchase history and business rules.
The result is AI that consumers can understand, retailers can trust and enterprises can confidently deploy at scale.
Building the Industry’s Most Trusted AI Commerce Platform
With today’s announcement, Rezolve has now systematically addressed what it believes to have been the three biggest obstacles to AI reaching its full commercial potential.
The first was accuracy. Rezolve addressed this through its proprietary brainpowa™ architecture, dramatically reducing the hallucinations that have limited the use of generative AI in enterprise commerce.
The second was accountability. Rezolve introduced TraceWare™, providing auditable tracking of autonomous AI agent activity and allowing every AI action to be monitored, verified and inspected.
Today, Auditable AI delivers the third pillar: transparency. Every recommendation can now be explained in language that consumers, retailers and regulators alike can understand.
Together, brainpowa™, TraceWare™ and Auditable AI establish what Rezolve believes is one of the industry’s most comprehensive architectures for trusted enterprise AI.
Our vision has never been simply to build smarter AI. Our objective has always been to build AI that enterprises can trust.
Over the years we have systematically addressed the three biggest challenges facing enterprise AI adoption. First we tackled hallucinations through brainpowa™. Then we introduced TraceWare™ to make autonomous AI actions fully auditable. Today we complete the next stage of that journey with Auditable AI, enabling every recommendation to be explained in a way consumers understand and businesses can verify.
We believe trusted AI, not simply intelligent AI, will define the next generation of global commerce.
Daniel M. Wagner Founder, Chairman and CEO | Rezolve Ai.
Transparency That Drives Commercial Performance
Beyond improving consumer confidence, Auditable AI has been designed to improve measurable commercial outcomes.
When shoppers understand why a product has been recommended and can see that recommendation is based on their own stated preferences, budget and previous interactions, they are significantly more likely to trust the recommendation and complete a purchase.
The technology also provides retailers with greater visibility into AI behaviour, helping improve governance, reduce operational risk and support evolving regulatory expectations surrounding explainable artificial intelligence.
Unlike conventional large language models, Auditable AI proactively identifies uncertainty and requests clarification whenever customer intent cannot be confidently determined, reducing the likelihood of inappropriate recommendations.
The architecture is model-agnostic, allowing it to operate across multiple AI systems while maintaining consistent standards of transparency and accountability.
Independently reviewed research conducted by Rezolve Ai Labs[1] demonstrated a 3.7x improvement in transparency compared with conventional large language model architectures.
AI systems are increasingly capable of producing excellent recommendations. The next challenge is enabling people to understand why those recommendations have been made.
Our research demonstrates an architecture that allows AI to communicate its reasoning in a way that is transparent, verifiable and understandable without compromising performance. That capability represents an important step towards trustworthy AI for real-world commerce.
Professor Senka Krivic Research Lead | Rezolve Ai Labs
The research underpinning Auditable AI has been accepted for presentation at the International Conference on Social Robotics (ICSR) 2026 in London, one of the leading international conferences focused on trustworthy human-centred artificial intelligence.
Auditable AI will be integrated into Rezolve’s Brain Suite platform alongside brainpowa™ and TraceWare™ as part of the company’s continuing strategy to deliver enterprise AI purpose-built for global commerce.
Notes to Editors
Professor Senka Krivic, Research Lead at Rezolve Ai Labs, presented the paper: “Mental-Model-Aware Retail Robots: Neuro-Symbolic Explainable Assistance for Trustworthy Human-Robot Commerce”, Krivic, Halilovic, Carvalho, Ahmad (2026) at The International Conference on Social Robotics (ICSR) in London, July 2026. Read more.
About Rezolve Ai
Rezolve Ai is a global leader in AI-powered commerce infrastructure, enabling retailers, brands, and platforms to deliver intelligent, personalized, and frictionless shopping experiences at scale. Through its proprietary Brain Suite and agentic commerce capabilities, Rezolve Ai is redefining how consumers discover, engage, and transact in the digital economy. For more information, visit rezolve.com.
About Rezolve Ai Labs
Rezolve Ai Labs (“RAILS”) is the research division of Rezolve Ai. RAILS conducts foundational and applied research across human-robot interaction, explainable AI, and trustworthy machine learning, to bring rigorous and ethical AI into real-world commercial settings.
[1] Mental-Model-Aware Retail Robots: Neuro-Symbolic Explainable Assistance for Trustworthy Human–Robot Commerce; Senka Krivic, Rezolve Ai Labs (Rezolve Ai Plc) and Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina; Amar Halilovic, Ulm University, Ulm, Germany; Diogo S. Carvalho Rezolve Ai Labs (Rezolve Ai Plc), and Salman Ahmad Rezolve Ai Labs (Rezolve Ai Plc).
