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AI-driven benchmarking and
red teaming for enterprise readiness

We tailor intelligent systems that solve real-world challenges, empowering organisations across all sectors to move faster, work smarter, and lead with confidence. Smarter technology. Sharper insights. Powered by AI.
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Navigating the challenges of AI safety and benchmarking.

Red teaming strengthens security and resilience while benchmarking drives operational and performance excellence. Most organisations prioritise one over the other, however each comes with its own set of challenges.

  1. Benchmark across diverse AI modalities

    Without a unified benchmarking system, users find it hard to compare model performance across different datasets and modalities — text, speech, and video. This creates hurdles in internal collaboration and knowledge sharing, resulting in slower innovation.

  2. Multiple vulnerabilities in Large Language Models (LLMs)

    LLMs are prone to prompt injection attacks, misinformation, and biased or harmful content. Current red teaming efforts require significant human input to identify and probe vulnerabilities. While automated systems can help, they often struggle with complex attack scenarios in an ever-evolving threat landscape.

Two solutions. One unified strategy for AI.

To address the trade-offs between performance and safety, we developed two distinct yet complementary evaluation solutions. Together, they provide a holistic view of AI systems. By adopting both, organisations can overcome the limitations of each and build more resilient, high-performing models.

Benchmarking Platform

Enhanced Collaboration

Drives continuous improvement through the internal sharing and development of AI models.

Benchmarking Platform

Model Submission and Leaderboard

Provides transparent pipeline for submitting and comparing models via intuitive leaderboards.

Red Teaming Framework

Vulnerability Detection Framework

Uses advanced analytical techniques to systematically identify weaknesses in LLM responses.

Red Teaming Framework

Adaptive Resilience Enhancement

Generate datasets to adversely test and improve model robustness.

Red Teaming Framework

Scalable Automation

Enables lean organisations to scale red teaming resources according to model testing needs, with adaptive learning capabilities to stay updated on emerging threats.

The engine behind safer, smarter AI.

AI Benchmarking Platform

A structured, industry-aligned evaluation platform that enables organisations to benchmark models across text, speech, and video using real-world datasets.

  • Retrieval Augmented Generation (RAG) benchmarking metrics – retrieval metrics, generation metrics, holistic metrics (e.g. RAGAS)
  • LLM benchmarking metrics
  • Multimodal model benchmarking metrics

AI Red Teaming Framework

A comprehensive two-pronged approach to reinforce AI safety:

  • Empirical testing
  • Compliant to industry standards (NIST, Mistre Atlas, OWASP)
  • Support diverse use cases and industries

Bridging gaps between safety, scale, and teamwork.

Used alone or together, these approaches provide a comprehensive strategy for improving performance and managing risk.

Unified AI benchmarking

Streamlines model evaluation across modalities, fostering better collaboration and faster innovation.

Enhanced AI Safety

Proactively identifies and mitigates vulnerabilities, ensuring responsible AI deployment.

Scalable red teaming

Reduces the need for extensive manual input by automating and scaling red-teaming efforts.

Make AI work for you. Build trust in every decision.

AI Assurance

As a Specialist Technical Testing Vendor under the AI Verify Foundation, we align with global standards for responsible AI and bring a rigorous approach to assurance from day one, with DeepAssure.

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