Melivera turns AI activity into evidence, so the answer is always yes, across every agent involved.
The risk isn't that your AI fails once. It's that you won't know when, why, or whether it's still happening, especially once one system starts acting on another's output. By the time it surfaces, it's already a regulator's question or a customer complaint, not a routine check.
The AI itself changes over time: what it was trained on, what it's connected to, how it's used, what data it receives. What passed review at launch may not reflect what's running today.
When a regulator, auditor, or customer asks "why did the system do that", teams need an answer they can produce quickly, not one they have to reconstruct.
Testing and red-teaming happen before go-live. Few teams have a reliable way to watch how a system actually behaves once it's live and making decisions.
When one AI system relies on another, whatever was verified for the first one is invisible to the next. Each new connection resets the risk, and no one owns the full picture.
Melivera is designed to sit at runtime, turning what your AI systems do, and the approvals behind it, into evidence you can prove, audit, and share, action by action and agent by agent.
As AI systems delegate work to other agents and tools, Melivera is designed to track what happened and who or what was involved across every handoff, so a chain of agentic actions can be held to the same standard as a single decision.
Every output is checked against the rules that matter for your sector as it happens, not sampled after the fact.
Define the boundaries your AI systems should operate within, and turn whether they held into evidence you can point to, across models, vendors, and agents.
Whether a human reviewed and approved a high-risk action becomes part of the record, not an assumption left out of it.
Every action is captured in a form designed to hold up for internal governance, auditors, regulators, and even other AI systems, not just sit in a log.
Critical systems like finance, healthcare, and legal services share something in common: the cost of an unverified AI output is measured in real harm, real money, or real liability.
From credit decisions to trading and advisory workflows, AI outputs need to be explainable to regulators and defensible to clients.
Clinical and administrative AI tools support decisions that touch patient safety. Oversight has to be continuous, not a one-time approval.
Legal AI has to meet a standard of accuracy and accountability that matches the profession it serves, on every matter, every time.
Melivera is designed to work alongside the AI systems you already run, complementing your existing models, vendors, and workflows rather than replacing them.
Melivera is designed to watch how your AI systems behave in production: what they're asked, what they answer, and in what context.
Each output is checked against the policies, regulations, and standards specific to your sector, in real time.
When something falls outside approved boundaries, it's flagged and, where needed, escalated to a human, with every decision recorded as evidence.
The same loop is designed to hold whether it's one model answering a question or a chain of agents handing work to each other, with verification carried across every hop.
Trust isn't a feature you bolt on later. It has to be built into how AI systems are run, from day one.
Trust has to hold up every time a system runs, not just on the day it was reviewed.
Your AI stack will change. Trust infrastructure should work across whatever you run today and whatever you adopt next.
A single verified action isn't enough on its own. When agents hand work to each other, trust has to carry across every step, or the chain is only as strong as its weakest link.
Every safeguard is designed to be explained to a regulator, an auditor, or a client, not just to an engineering team.
We build with the compliance, risk, and clinical or legal teams who ultimately answer for what the AI did.
Melivera is being built by a founding team spanning AI and security research, finance, and security-industry operations.
Security and AI systems researcher with a background spanning University of Cambridge, Nokia Bell Labs, and Google. Co-invented a secure whistleblower communication system now used by a major newsroom.
Chartered accountant and MBA with over a decade in commercial finance, FP&A, and treasury across listed and high-growth companies, most recently leading finance for a global digital division. Now runs an independent finance advisory practice.
Finance and operations leader with a decade in the security services industry. Currently CFO of a multi-million euro security company, spanning financial planning, operational process design, and technology adoption.
Entrepreneur leading a security systems integration company, driving business strategy, revenue growth, and operational delivery. Focused on building scalable security and technology ventures.
We're having 30-minute discovery conversations with AI platform teams, regulated institutions, CISOs, researchers, regulators, and standards bodies, to understand where AI trust breaks down. If that's relevant to your work, we'd appreciate the chance to learn from your experience.
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