Experience & capability

Representative experience behind NextGen Informatics

Anonymised examples of experience and capability brought into NextGen Informatics by its directors and specialist network.

These are anonymised examples of experience and capability brought into NextGen Informatics by its directors and specialist network. They illustrate the kind of work the team can deliver — they are not presented as completed NextGen Informatics contracts unless separately supported by a client reference, and no client names or confidential details are disclosed.

Capability examples

Representative leadership experience

Enterprise technology / financial servicesCapability example

Enterprise DevOps & secure delivery

Challenge: Release cycles depended on manual steps and a small group of specialists, with limited automated security or vulnerability checks in the pipeline.
Approach: Design and improvement of CI/CD pipelines; automated deployments; SAST/DAST and vulnerability integration; artifact and repository management; configuration automation; container adoption; production troubleshooting; mentoring and engineering standards.
Outcome: More consistent, auditable releases with security checks integrated earlier in the pipeline, and engineering standards embedded through mentoring.

CI/CD pipelines
SAST/DAST
Containers
Configuration automation

Enterprise technologyCapability example

Solution architecture & modernisation

Challenge: Teams needed scalable, secure and cost-aware designs across a mix of cloud and on-premises environments, with governance forums to assess risk and impact.
Approach: High-level and low-level design and roadmap development; governance forum participation; risk and impact assessments; microservices/container adoption; technology evaluation and proofs of concept.
Outcome: Clearer, better-governed architecture decisions supporting a practical transition path from legacy to modern platforms.

HLD / LLD
Microservices
Containers
Cloud & on-premises architecture

Enterprise technologyCapability example

Cloud, infrastructure & cost optimisation

Challenge: Cloud and container environments needed repeatable, automated provisioning alongside better visibility into utilisation and spend.
Approach: Automated provisioning of IAM, networking, compute, storage and container environments; utilisation analysis and rightsizing; repeatable configuration; secure hybrid patterns and cost-reduction initiatives.
Outcome: Repeatable, secure infrastructure delivery paired with rightsizing and cost-reduction actions.

IaC
IAM
Hybrid cloud
Cost optimisation

Enterprise technologyCapability example

SRE, monitoring & operations

Challenge: Operational teams needed better visibility into system health and faster root-cause diagnosis to reduce time spent on reactive support.
Approach: Monitoring, alerting and logging implementation; system administration; incident response; root-cause troubleshooting; service reliability, capacity and performance work; production support and automation of operational tasks.
Outcome: Improved visibility into service health and a more structured approach to incident response and recovery.

Monitoring & alerting
Incident response
Capacity planning
Operational automation

Financial servicesCapability example

Legacy & modern engineering

Challenge: Critical mainframe systems needed to keep running reliably while new APIs, channels and cloud services were introduced around them.
Approach: Combined COBOL, JCL, DB2 and mainframe exposure with modern Java, APIs, containers, cloud and integration work — supporting practical, staged transition rather than a disruptive full replacement.
Outcome: Continued stability of core systems alongside incremental modernisation of surrounding capability.

COBOL
JCL
DB2
Java
APIs
Containers

Cross-industryCapability example

AI-assisted engineering & innovation

Challenge: Teams wanted to evaluate AI-enabled development and automation approaches without compromising governance or engineering discipline.
Approach: Evaluation and prototyping of AI-enabled development and automation approaches, with emphasis on business value, engineering productivity, governance and safe adoption.
Outcome: A structured, evidence-based approach to piloting AI-enabled engineering practices before wider adoption.

AI-assisted development
Automation prototyping
Governance design

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