Ensure AI safety and performance through
trusted evaluation

Implement a platform to assess AI systems for potential
vulnerabilities, robustness and explainability to build a safe,
responsible, and high-performing AI deployment.

What’s getting in the way?

An internal productivity tool powered by AI showed promise, but leaders knew that without accuracy, reliability, and proper safeguards against hallucinations or data leaks, adoption would be risky and trust would be hard to earn.

  1. Accuracy and completeness

    Tools must reliably capture critical facts. (dates, locations, etc.)​

  2. Hallucination risks

    LLMs often infer connections rather than quoting source text​.

  3. Data leakage and inappropriate content

    Risk of sensitive data exposure via adversarial prompts and generation of unsafe, biased, or unprofessional content.​

Trustworthy AI, validated.

Our framework ensures your large language models perform safely and reliably, giving you peace of mind in AI deployment.

DeepAssure Platform

A custom red-teaming toolkit that automates adversarial prompts to test classification, summarisation, and Q&A modules.​

Multi-Modal Evaluation

Uses metrics like precision-recall-F1, summarisation integrity, reject-score, and attack success rate to assess bias, accuracy, and adversarial leakage.

Hybrid Testing Strategy

Semi‑automated generation of datasets, followed by human expert review and iterations, ensuring both breadth and quality in test coverage.​
Note: Due to confidentiality, we are unable to share the actual visual of our customer’s platform.

Reliable AI, guaranteed.

Trusted AI that works, safely, every time.

Operational
Trust

Demonstrated resilience and reliability of the LLM assistant increasing stakeholder confidence.​

Rigorous Safety Assurance Pipeline

Established a repeatable assurance framework for future AI deployments by combining synthetic dataset generation with human expert review.

Valuable AI Deployment Insights

Lessons on hallucination limits, adversarial vulnerabilities, and dataset diversity lay the groundwork for safer, more dependable GenAI solutions.​

Let’s get started

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