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Generative AI
Cloud
Testing
Artificial intelligence
Security
Move from risk to confidence, Right here. Right now.
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Organizations are rushing to adopt AI, but many struggle to determine which risks matter most. Beyond hallucinations, AI can fail through bias, inconsistency, security, reliability, and compliance issues. Without a structured approach, teams risk focusing on the wrong problems while overlooking the failures that could have the greatest business impact.
How do we ensure AI is safe, trustworthy and reliable?
Sogeti helps organizations move from AI uncertainty to AI confidence through a risk-based, business-driven quality engineering approach. We begin with two simple questions: What does your system do? and What’s the worst that could happen if it fails? Using these answers, we assess AI risks in the context of your business priorities and identify the risks that deserve the greatest focus.
Our approach combines the Evaluate.AI™ methodology with structured risk assessment to evaluate AI systems against 64 AI-specific risks across six quality dimensions, helping organizations prioritize testing, assurance, and governance efforts where they will have the greatest impact. The result is a clear AI risk profile, prioritized recommendations, and a practical roadmap to trusted, scalable AI adoption.
Sogeti helps overcome these challenges by transforming AI ambition into measurable business outcomes through comprehensive AI Quality Engineering, continuous testing, automated validation, and risk-based assurance across the AI lifecycle.
Our offer leader, Padmaja Alapati, shares how Sogeti is helping organizations tackle unpredictable output, hidden bias and lack of quality benchmarks with a robust, risk-driven approach.
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Sogeti’s AI Trust and Assurance helps organizations identify AI risks, automate testing and validation, and continuously monitor performance to ensure AI systems remain reliable, safe, and aligned with business goals throughout their lifecycle.Outcome: A risk-focused, scalable, and continuously monitored AI quality strategy.
A comprehensive AI Trust & Assurance.Risk driven Profiling methodology, encompassing 6 quality characteristics, 26 sub-dimensions, and 64 defined risks, with risks mapped to objectives and measurable criteria with defined thresholds.
Refining user stories with clear acceptance criteria and a human validated golden dataset. Specialized test framework with best practice techniques to ensure accurate evaluation. Includes prebuilt AI agents for testing AI systems.
Our Observability Framework provides continuous, real-time visibility into AI system behavior, performance, and outputs across the model lifecycle. It enables early detection of drift, anomalies, and emerging risks, turning monitoring into a foundation for sustained AI trust and assurance.
How does it work? We run your system through a four-step evaluation process:1. System Profiling2. Business Impact Scoring3. Risk Identification4. Risk classification & coverage Outcome: A comprehensive AI Quality Assessment report that documents your risk profile & test strategy.
The next steps after the assessment involve defining success through clear, measurable quality criteria & acceptance thresholds. The process then moves into setup & execution, where automated testing & evaluation to validate AI systems using structured datasets & metrics using EvalOrch™.Finally, a quality verdict determines the system’s readiness for deployment based on evidence & risk. Looking to build an AI system that you can truly trust?
Turn risk into measurable outcomes, Right here. Right now.
Gaps across the end-to-end AI lifecycle- including limited automation, insufficient monitoring, undefined quality metrics, and a shortage of skilled talent – are driving the need for integrated, risk-driven AI testing and continuous assurance.
As enterprises scale AI adoption, the focus has shifted from one-time deployment to continuous testing, monitoring, and risk management. Increasing concerns around bias, security, and regulatory compliance make AI quality engineering essential for production-ready systems.
AI systems face challenges such as non-deterministic behavior, hallucinations, bias, silent failures, and compliance risks—along with difficulties in defining quality metrics and prioritizing risks effectively.
Sogeti applies a risk-based Quality Engineering approach that combines the Evaluate.AI methodology and EvalOrch platform, enabling continuous testing, automated evaluation, and end-to-end quality assurance.
Through the TMAP framework (Methodology, Accelerators, People), covering:
By evaluating 64 AI-specific risks across six quality dimensions, using automated “golden dataset” validation and LLM-based scoring to ensure consistent, measurable AI quality from planning to production.
Sogeti offers an AI Quality Assessment workshop (5-10 days) to identify risks, prioritize testing, and accelerate AI adoption with confidence.
Global Head of Quality Engineering & Testing
For all enquiries, please use the form below:
CTO for Quality Engineering & Testing, Sogeti
Senior Director
Director Innovation Lead (QET)
Sr. Manager ( QET) Lead Innovator
VP Global CTO Applications, Cloud & Experience
Global Head of Applications, Cloud & Experience
Cloud GTM Service Lead
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30 min
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Enterprise GenAI & Cloud Architect
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