Nagarro

nagarro.com

Nagarro is a technology consulting and engineering provider rather than a research publisher or product-only vendor. Buyers can commission advisory work, implementation and selected operational services. Its AI practice explicitly offers model development and deployment, while cloud, cybersecurity and SAP pages distinguish planning from implementation and ongoing operations. Proprietary accelerators also appear in its portfolio, but their availability does not by itself establish licensing terms or implementation scope. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

AI strategy and use-case advisory

Advisory. Helps enterprises define an AI strategy, discover and prioritize use cases, and develop detailed use-case designs. This is a planning engagement, separate from model implementation. ^[2]

AI model implementation

Implementation. Implements custom or pretrained models for applications including semantic search, personalization and decision intelligence. ^[2]

AI-centric product development

Implementation. Engineers products in which AI is a central capability, rather than treating model development as an isolated analytical exercise. ^[2]

MLOps engineering

Implementation. Builds deployment and lifecycle engineering for AI applications using cloud architectures, continuous learning and monitoring. ^[2]

Generative AI platform engineering

Implementation. Designs and scales production AI platforms, including LLM and agentic systems, with governance and data-residency architecture. ^[3]

Digital product and software engineering

Implementation. Develops digital products from concept through launch. The practice covers software and platform engineering, UX engineering, Cloud/DevOps engineering, and quality engineering with test automation. ^[1]

Cloud strategy and architecture advisory

Advisory. Produces cloud strategies, business cases, architecture reviews, application assessments, migration plans and operating models. Cloud-selection scope includes AWS, Azure and GCP. ^[3]

Cloud migration and modernization

Implementation. Implements landing zones, datacenter exits, VMware migrations, application replatforming and refactoring, database modernization and Kubernetes adoption. ^[3]

Cloud reliability and optimization engineering

Implementation. Provides FinOps, observability, site reliability engineering, DevSecOps and performance engineering. Buyers should distinguish this engineering scope from any contracted ongoing operations coverage. ^[3]

Cybersecurity advisory and assessment

Advisory. Provides cybersecurity strategy, security architecture, application, API and mobile testing, penetration testing and vulnerability assessments. AI-security governance is distinct from developing AI models. ^[4]

Identity modernization

Implementation. Designs and implements identity systems. The cybersecurity page includes a school-network example involving Entra ID implementation across more than 200 applications. ^[4]

Managed identity and access management

Managed services. Offers managed IAM services, distinct from initial identity-system implementation. Operational responsibilities and service levels require agreement in the engagement scope. ^[4]

SAP landscape and S/4HANA advisory

Advisory. Assesses ERP landscapes and develops S/4HANA roadmaps to support enterprise-platform planning. ^[5]

SAP implementation and testing

Implementation. Supports S/4HANA Cloud onboarding, SAP supply-chain and HCM work, testing, and integrated-toolchain implementation. ^[5]

SAP operations

Managed services. Provides day-to-day SAP operations and operation of the integrated toolchain. Support coverage and operational boundaries need contractual confirmation. ^[5]

Forge proprietary accelerators

Software product. Forge presents proprietary platforms and accelerators including Forcastra++, Mosaic OS and NIA++. This establishes product/IP availability, not a particular licensing model or separately purchasable implementation engagement. ^[6]

Company details

Track record

For Victaulic, Nagarro reports implementing an AWS-hosted conversational support agent using retrieval-augmented generation and its NIA platform. The documented stack includes Textract, OpenSearch, DocumentDB, S3, Lambda and Bedrock with Claude Sonnet. Nagarro reports 600–700 engineering hours saved annually and response accuracy above 90%; these are case-study claims, not independently verified benchmarks. ^[7]

For Audi, Nagarro reports conceptualizing and implementing an API-management solution with an OpenAPI-based layer, automated service registration, a developer portal and an authorization server. The case identifies AWS, OAuth 2.0 and OpenID Connect. ^[8]

An unnamed automotive distributor case describes data consolidation, a warehouse, dashboards and ML models for lead-conversion scoring and segmentation. An unnamed beverage-manufacturing case describes a serverless MLOps workflow using Spark on EMR and SageMaker training. Its edge deployment remained in progress; the case does not establish completion of every planned phase. ^[9] ^[10]

Engagement and delivery

The Victaulic engagement describes a discovery workshop and tailored demonstration before implementation. The automotive lead-management case starts with process and data-maturity assessment. These examples show assessment-to-build engagement patterns, but do not establish a mandatory delivery method for every project. ^[7] ^[9]

Company background

A June 2026 announcement describes a signed business-combination agreement involving Persistent and an intended voluntary public takeover offer subject to conditions. The supplied research does not establish completion, and Persistent’s capabilities are not attributed to Nagarro here. ^[11]

Scope and limitations

The supplied evidence does not establish standard pricing, minimum project sizes, staffing commitments or universal support coverage.
Two ML delivery cases are anonymous, and the manufacturing case explicitly identifies unfinished edge-deployment work.
Reported case-study outcomes are Nagarro’s claims and were not independently verified.
Accelerator licensing, source-code access, customer ownership rights and exit terms are not established by the product evidence.

Questions to ask

Which comparable client references can you provide, and who owns production deployment and acceptance?
What model-evaluation thresholds, monitoring responsibilities and retraining obligations will be contractual?
Which cloud, model-provider and data-residency constraints apply to the proposed solution?
What accelerator licensing, source-code access and exit arrangements are included?
What staffing, support hours, incident ownership and service levels are committed for implementation and operations?

Overview