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Generative AI
Cloud
Testing
Artificial intelligence
Security
Move from risk to confidence, Right here. Right now.
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How do we ensure AI is safe, trustworthy and reliable?
60% of organizations cite hallucination and reliability concerns in AI adoption. Sogeti helps organizations overcome these challenges and turns AI ambition into measurable business outcomes through a comprehensive Quality Engineering approach for AI.
Combining our proprietary Evaluate.AI™ methodology, the EvalOrch™ platform, and continuous testing, automation, and monitoring, we enable trusted and scalable AI. Built on 64 AI-specific risks across six quality dimensions and powered by automated golden dataset validation and LLM-driven scoring, our approach provides end-to-end quality assurance from planning and development to deployment and production monitoring.
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.
How does it work? We run your system through a four-step evaluation process:– System Profiling– Business Impact Scoring– Risk Identification– 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?
Turning AI quality into tangible business results. (based on actual client implementations)
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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.
Observability framework for Analysis and recommendations, complete with production monitoring and feedback for continuous refinement of test Specifications.
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.
Head of QA, UK
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CTO for Quality Engineering & Testing, Sogeti
