Infosys

infosys.com

Infosys is primarily a consulting and IT services business, rather than a research publisher or software vendor. The reviewed offerings span technology advice, engineering implementation, and managed security operations. Buyers should distinguish these delivery models when defining a contract: AI adoption advice is separate from building and scaling AI systems, while cloud assessment is separate from migration execution. Its Topaz portfolio combines services, solutions, and platforms, so the portfolio name alone does not define a delivery commitment. ^[1] ^[2] ^[3] ^[4] ^[5]

Services and delivery

Enterprise AI strategy advisory

Advisory. Helps organizations identify AI use cases and shape adoption strategies before scaling investment. Documented advisory scope includes responsible implementation planning, sovereign-AI risk assessment, and roadmaps. Buyers should specify the required assessments, priorities, and planning deliverables separately from implementation. ^[2]

Enterprise AI implementation and scaling

Implementation. Provides AI model design, AI-powered customer experiences, and engineering to move beyond proofs of concept. The Enterprise AI practice combines consulting with data scientists, generative-AI architects, engineers, and Topaz delivery capabilities to support enterprise deployment. ^[2]

Databricks AI and machine-learning services

Implementation. Offers AI/ML, generative-AI, and responsible-AI services on Databricks. The offering names Databricks MosaicML and DBRX. Buyers should distinguish contracted engineering work from associated software frameworks and platform components. ^[6]

Databricks data engineering and governance

Implementation. Provides data-estate migration and modernization, data engineering, and governance/security services within its Databricks offering. Unity Catalog is among the named technologies. These services support data-platform work but are distinct from the separately offered AI/ML services. ^[6]

AI engineering and deployment infrastructure

Implementation. Applied AI Cloud combines AI engineering services with access to AI hardware and open-source software on hybrid infrastructure. Its engineering lifecycle supports experimentation through enterprise deployment, including shared infrastructure for machine-learning and deep-learning operations. ^[7]

Cloud migration and modernization advisory

Advisory. Assesses applications and plans cloud migration and modernization. Advisory scope includes strategy, assessment, migration planning, and cloud cost management. This work should be scoped separately from application changes and migration execution. ^[3]

Application and database cloud migration

Implementation. Implements application and database rehosting, remediation, and replatforming. Documented scope includes platform-as-a-service adoption and migration to managed Kubernetes, providing execution services beyond assessment and planning. ^[3]

Cloud-native application modernization

Implementation. Reengineers monolithic applications for cloud-native architecture. Implementation scope includes containerization, hybrid cloud, serverless adoption, modern databases, and cloud DevSecOps for organizations changing application architecture rather than simply relocating workloads. ^[3]

Managed cybersecurity operations

Managed services. Operates security monitoring and supports security operations center tools. Documented scope includes threat detection and response, vulnerability assessment and penetration testing, threat intelligence, infrastructure and cloud-security controls, and operational-technology integration and monitoring. ^[4]

Knowledge Institute research and content

Research. Publishes business-technology research, perspectives, podcasts, and related content. This material is separate from Infosys implementation services and does not itself establish a technology delivery commitment. ^[8]

Company details

Technology and platforms

Topaz is described as a set of services, solutions, and platforms, not one uniform product. The Databricks offering also includes a low-code Enterprise AI Workbench and an AI Data Factory combining software and services. Procurement should separate platform access, engineering deliverables, and operating responsibilities. ^[5] ^[6]

Engagement and delivery

The Telenet case describes a dedicated modernization office and delivery squads alongside application assessment, modernization planning, and implementation. It provides evidence of a governance-and-delivery structure for that engagement, rather than establishing a standard staffing model for every Infosys project. ^[9]

Track record

For Siemens, Infosys reports implementing an LLM-based solution to classify and summarize tax-related Microsoft Viva Engage communications using in-context prompting. Reported results include approximately 90% binary-classification accuracy, 85–90% multiclass accuracy, and classification in under five seconds per post. These are case-specific results reported by Infosys, not independent benchmarks. ^[10]

For an unnamed US fashion company, Infosys reports implementing generative-AI functions for user-story refinement, test-case generation, code/SQL explanation, code lineage, and developer assistance. The implementation used AWS Bedrock with AWS S3, Confluence, Jira, and ServiceNow. Infosys reports up to 80% effort savings in test-case generation and up to 30% shorter development cycles. ^[11]

The Telenet case documents deployment of modernized, containerized applications to OpenShift/private cloud and AWS/public cloud, Oracle-to-PostgreSQL migration, GitLab CI/CD reuse, and Cucumber BDD test automation. The stated ambition to modernize and migrate more than 300 applications is prospective, not a completed migration count. ^[9]

Scope and limitations

The supplied evidence establishes service scope but not pricing, contractual delivery ownership, warranties, production acceptance criteria, or support SLAs.
Applied AI Cloud documents engineering and infrastructure scope, but does not establish a fully outsourced model-operations contract or a particular production SLA.
The anonymous fashion-company case does not disclose the customer identity, evaluation methods, or production-support arrangements. Its reported outcomes should not be treated as general performance promises.
The older AI-automation consulting URL supplied little substantive offering text and was not used to establish capability.

Questions to ask

Which advisory, implementation, licensing, infrastructure-consumption, and operating responsibilities will have separate statements of work?
Who owns delivery, acceptance testing, and post-launch support, and what service levels will be contractual?
Can Infosys provide references with comparable application estates, regulatory requirements, and deployment constraints?
For AI work, what evaluation methods, acceptance thresholds, data/model ownership terms, and production monitoring responsibilities will apply?
Which platform dependencies and licensing costs apply, and what migration or exit arrangements will be included?

Overview