Working with Infosys & AWS
Arguably the biggest challenge facing organizations as they consider how best to improve the quality of data being used to train their AI tools is the scarcity of real-world examples and data points to learn from. That’s why it’s so beneficial to work with partners who have extensive experience and expertise in helping organizations get their data ready for AI.
Infosys Topaz is an AI-first set of services, solutions and platforms using generative AI technologies. It helps amplify the potential of humans, enterprises and communities to create value from unprecedented innovations, pervasive efficiencies and connected ecosystems.
It brings the advantage of 12,000+ AI assets, 150+ pre-trained AI models, 10+ AI platforms steered by AI-first specialists and data strategists, and a ‘responsible by design’ approach that is uncompromising on ethics, trust, privacy, security and regulatory compliance.
Leveraging Infosys applied AI framework to build an AI-first core that empowers people to deliver cognitive solutions, Infosys Topaz help enterprises accelerate growth, build connected ecosystems and unlock efficiencies at scale.
AWS provides enterprise-grade cloud infrastructure to build reliable and trusted AI applications by enabling complete control over data across ingestion, analysis, delivery, and presentation, supported by robust governance capabilities. This foundation ensures that organizations can power AI solutions with properly managed, secure, and compliant data that delivers accurate, contextual, and trustworthy outcomes. At the core is automated data quality management, where AI/ML-driven capabilities proactively recommend data-quality rules tailored to specific datasets, eliminating complex coding while supporting preventive, proactive, and reactive monitoring to detect issues before they impact AI applications, thereby transforming data quality into a strategic advantage. Centralized metadata management and governance further enable users to easily discover, understand, and trust data through automated profiling, comprehensive lineage tracking, and transparent lifecycle visibility, while still allowing seamless data sharing across teams under appropriate governance controls.
Finally, application-layer safeguards ensure quality where it matters most, in AI outputs by using guardrails to detect hallucinations through data-grounding checks, block harmful or irrelevant content, and, for critical use cases, automatically identify, correct, and explain factual claims, ensuring AI responses meet enterprise-grade quality standards.
