Demetris Gerogiannis is an AI entrepreneur and ecosystem builder with a PhD in Computer Vision. He is the co-founder of ai&me, a company that offers automated AI red teaming and QA assessments for GenAI apps. He is a founding member and Chair of the Association of Artificial Intelligence – aicatalyst and organizes the AIandBeers meetup, a nationwide event to promote trustworthy AI and related skills. He also serves on the Steering Committee of the Joint Focus Group AI of the EIT AI Community and the Digital SME Alliance. We met with Dimitris to talk about AI&ME and his personal journey. Here's what he told us. 

Tell us a bit about AI&ME — how did the idea come about, and what made you realize there was a market need for your solution?

The idea for ai&me emerged from our direct experience working with enterprises that were scaling LLM applications without sufficient safeguards or validation mechanisms. While runtime filters provided a minimal baseline, they offered limited protection and failed to capture real business context. We saw that most teams struggled to implement continuous testing or integrate GenAI quality assurance within existing software development life cycles.

This gap was especially evident in regulated industries like financial services, where compliance, risk mitigation, and explainability are critical. These organizations needed a platform that not only detected vulnerabilities but could also support them pre-, during, and post-deployment. ai&me was built to fill this gap—offering contextualized QA, red teaming, and AI security aligned with real business logic. Our traction with early adopters confirmed the demand for this end-to-end approach.

What sets AI&ME apart from similar solutions in the market? What specific value do you deliver to your customers and partners?

ai&me offers a differentiated approach to GenAI security by focusing on contextual, full-lifecycle AI assurance rather than point solutions. We deliver enterprise value in five core areas:

• End-to-end coverage: From pre-production adversarial and behavioral testing to runtime AI firewalls and post-deployment auditing.
• Business-aligned testing: Each test scenario is grounded in the organization's unique use case, enabling precise, non-generic assessments.
• RedAgent and Red Teaming-as-a-Service: A natural language interface and community-driven testing programs empower technical and non-technical teams alike to run rigorous evaluations.
• Seamless integration and cloud-agnostic deployment: Enterprises can embed our tooling into their CI/CD workflows across AWS, Azure, GCP, or on-prem environments with minimal friction.
• Human-AI synergy: Our LLM-as-a-judge model incorporates domain-specific feedback, enabling continuous improvement and high-confidence evaluations.

This combination allows enterprise customers to scale their GenAI systems securely and reliably, with evidence-based insights for both internal and regulatory stakeholders.

What has been your biggest challenge so far — and how did you overcome it (or how are you addressing it)?

The biggest challenge has been navigating the rapid commoditization of basic safety features by major cloud vendors. Tools like AWS Bedrock Guardrails or OpenAI’s Moderation API are bundled into platforms and set expectations for “free” or low-effort safety.

We address this in three ways:

1. Deep differentiation — offering domain-specific evaluators and custom LLM-as-a-judge configurations that go far beyond generic filters.
2. Platform ubiquity — integrating seamlessly with dev workflows, CLI tools, and marketplaces to reduce adoption friction.
3. Human value — crowdsourcing real-world attacks and promoting human-in-the-loop QA, which large cloud providers cannot easily replicate.

What are the most important lessons you’ve learned along the way?

One key lesson is that AI assurance is highly context-dependent. Organizations don’t just need tools—they need solutions that adapt to their workflows, risk models, and regulatory environment.

We’ve also learned that speed matters. Shipping quickly and iterating with feedback from enterprise users has been crucial, especially in high-stakes sectors like finance and healthcare. These organizations value transparency, auditability, and actionable results over theoretical safety benchmarks.

Finally, compliance is non-negotiable. From GDPR to internal governance protocols, our platform has been designed from the ground up to support auditable logs, RBAC, encryption, and EU-hosted deployment—ensuring that enterprise risk and legal teams remain confident in what we deliver.

As demand for AI-driven solutions grows across industries, how can companies developing these technologies position themselves to deliver real value and scale effectively?

Delivering real value at scale requires more than model accuracy—it requires trust, resilience, and continuous validation. Here’s how we believe companies should position themselves:

1. Shift left: Integrate QA, red teaming, and safety testing early in the development process. Waiting until production introduces avoidable risks and costs.
2. Simulate real-world use: Testing against benchmarks isn’t enough. Systems must be stress-tested with realistic, adversarial, and compliance-sensitive scenarios.
3. Empower human oversight: High-stakes decisions cannot be left to models alone. A layered approach combining human insight with automated tools is essential.

At ai&me, these principles shape everything we build. We aim to enable enterprises not just to deploy AI—but to deploy it safely, responsibly, and at scale.