Endava

endava.com

Endava is a technology services provider rather than a research publisher or software-license vendor. Its documented scope extends from technology strategy and architecture through application development, platform implementation and ongoing operations. Dava.X offerings cover AI, cloud and core modernisation. Buyers can procure advisory, implementation and managed-service scopes, but should distinguish these explicitly: a roadmap does not establish implementation coverage, and a delivery accelerator does not establish a separately licensed product. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

Technology strategy and enterprise architecture

Advisory. Advises on technology strategy, enterprise architecture and data strategy. The offering supports roadmap development; implementation should be scoped separately. ^[1]

AI opportunity and strategy advisory

Advisory. Helps identify AI opportunities and plan enterprise adoption, including agentic AI. This is a commercial advisory offering, separate from model development or tool deployment. ^[4]

AI accelerator development

Implementation. Develops AI and data accelerators and new intellectual property for customer use cases. Standard packages, ownership rights and acceptance criteria require engagement-specific agreement. ^[4]

Enterprise AI-tool adoption

Implementation. Supports implementation and corporate rollout of AI tools such as ChatGPT Enterprise. This is adoption and integration work around third-party tools, not evidence of Endava owning their underlying platforms. ^[4]

Custom machine learning and computer vision

Implementation. Develops process-specific ML models and applications. A pharmaceutical engagement documents parameter prediction, liposome detection and characterisation, data validation, and interfaces explaining model conclusions to scientific users. ^[7]

Software engineering

Implementation. Builds mobile, enterprise, games and payment-infrastructure applications. Documented engineering disciplines include architecture, cloud application engineering, platform engineering, software security and testing. ^[2]

Legacy-system assessment and modernisation planning

Advisory. Assesses code, data, documentation, infrastructure and third-party tools to develop a modernisation strategy and roadmap. Assessment is distinct from transformation execution. ^[6]

Core modernisation implementation

Implementation. Executes transformation of legacy systems into a new digital core. The offering distinguishes implementation from assessment and subsequent operation; AI-assisted assessment tools do not make every engagement an AI service. ^[6]

Cloud adoption advisory

Advisory. Plans cloud adoption and platform direction, including hybrid and multi-cloud environments. Advisory is a separate scope from building platforms or operating them. ^[5]

Cloud platform engineering

Implementation. Designs, builds and integrates cloud platforms, with automation and optimisation. The service explicitly covers AWS, Azure and Google Cloud, including hybrid and multi-cloud delivery. ^[5]

Data engineering and analytics platforms

Implementation. Addresses inaccessible organisational data through engineering and platform services spanning business intelligence, data warehousing, big data, analytics and visualisation. This offering alone does not establish ML model development. ^[8]

Application and cloud managed operations

Managed services. Provides application management, managed cloud, service delivery and service-desk services. Customers can engage shared services or dedicated teams. ^[3]

Managed security operations

Managed services. Offers 24/7 security monitoring, threat detection, vulnerability management and incident response. Tooling, contractual response times and regulatory responsibilities require verification. ^[9]

Company details

Technology and platforms

An unnamed UK insurer’s project documents AWS platform automation using Ansible, CloudFormation, Lambda, Git, Docker and Jenkins, adapted for production security and compliance. This provides concrete cloud and DevOps delivery evidence beyond the cloud advisory offering. ^[10]

Compass is described as an internally developed agentic AI tool used in a ZEISS modernisation pilot within the customer’s Azure environment, using Azure OpenAI. Outputs included analysis, refactoring opportunities, backlogs and templates. Endava says outputs require validation; standalone licensing is not established. ^[11]

Track record

For Stem Inc., Endava describes an AI roadmap, proof of concept, algorithm selection, electricity-demand forecasting and model validation, supported by testing, cloud/IoT microservices and REST endpoints. Endava reports electricity-cost reductions of 60% in some cases, not a general expected outcome. ^[12]

An investment-firm pilot deployed Gemini Enterprise with Google Cloud Storage, BigQuery and Vertex AI. Work included ingestion pipelines, Active Directory authentication, testing, onboarding and knowledge transfer. Endava reports 65% shorter search time and 40% fewer research delays; enterprise-wide rollout is not established. ^[13]

For ZEISS Microscopy, Endava describes ongoing engineering support for ZEN software and ZEISS arivis Cloud, including deep-learning algorithms, image processing, particle analysis, data management and DevOps. These are customer product-engineering examples, not Endava-owned microscopy products. ^[14]

Scope and limitations

Some customer references are anonymous. The investment-search and Compass examples concern pilots and do not establish production maturity across other engagements.
The supplied evidence does not specify standard AI packages, acceptance criteria, or ongoing model-monitoring and retraining commitments.
Public descriptions do not establish project pricing, proposed team composition, contractual service levels or ownership terms for custom assets.
Compass is evidenced as a delivery tool, not a verified standalone licensed product.

Questions to ask

Which deliverables and acceptance criteria separate discovery, pilot, production implementation and ongoing operation?
Who owns custom code, models, accelerators and deployment assets, and what reuse rights apply?
Which platform licenses, infrastructure charges and customer-environment constraints sit outside the service fee?
Who owns model evaluation, monitoring, retraining and incident support after deployment?
Can Endava provide relevant production references and identify the proposed delivery team?
What coverage, escalation paths, service levels and exit assistance are included in managed services?

Overview