Use Case

Project Overview

ai&me was developed to address the critical need for enterprise-grade AI assurance. It secures generative AI applications across the entire lifecycle—pre-production, deployment, and post-deployment. Its goal is to reduce operational, compliance, and reputational risks while enabling organizations to scale AI responsibly.

Timeline & Status

Initial pilots in 2023 with financial institutions; currently in production deployments with enterprises such as Eurolife, NatWest, and Viva.com.

Objectives & Goals

• Provide contextual adversarial and behavioural testing before deployment.
• Enforce runtime AI firewall protections tailored to business needs.
• Enable retrospective risk analysis and compliance reporting.
• Support enterprise QA, compliance, and governance teams with shareable insights.

Expected Outcomes & Business Value

• Stronger AI risk posture and compliance confidence.
• Reduced cost of late-stage AI failures by shifting security testing earlier.
• Cross-functional alignment between technical, legal, and risk teams.
• Faster time-to-market with safer and more reliable AI deployments.

Impact Summary

• Business: Faster adoption of AI with reduced risk exposure.
• Technical: Seamless integration into CI/CD workflows and AI pipelines.
• Societal: Promotes responsible, trustworthy AI adoption.
• Regulatory: Enables compliance with GDPR, internal governance, and international standards.

Technical Stack & Deployment

• AI methodologies: Adversarial testing, LLM-as-a-judge, behavioural QA simulation.
• Tools / Frameworks: Proprietary red teaming engine, APIs, Git-based workflows.
• Deployment: SaaS (multi-tenant) or private cloud/on-prem for regulated enterprises.

Data Strategy

• Types: Synthetic data, conversational logs, adversarial prompts, evaluation reports.
• Formats: Structured logs, natural language inputs/outputs, compliance-ready PDFs.
• Sources: LLM interactions, customer-defined scenarios, feedback loops.

Solution Development & Challenges

The platform was designed to fill gaps left by generic runtime filters. Key challenges included overcoming commoditization by cloud vendors, ensuring seamless integration with enterprise workflows, and aligning with strict compliance requirements. ai&me addressed these with domain-specific evaluators, cloud-agnostic integration, and GDPR-ready deployments.

Results & Lessons Learned

• Successful pilots with financial institutions validated the platform’s value.
• Shareable, automated assessment reports accelerated compliance reviews.
• Lesson: AI assurance must be contextual, workflow-embedded, and human-in-the-loop to deliver lasting enterprise value.