Portfolio / Selected work
Explore how Aevis frames complex technology engagements: the operating context, the intervention, the decisions made and the evidence used to judge what changed.
Portfolio register
Filter by service to see how the work changes with the context. Each summary keeps the problem, intervention and designed outcome together rather than presenting a disconnected list of technologies.
6engagements
A phased operating-model transition joined service desk, infrastructure, endpoint and monitoring responsibilities without a disruptive big-bang cutover.
One accountable service view with routine work progressively automated.
Coverage, triage and remediation were organised around critical clinical services, with findings entering owned operational workflows instead of stopping at recommendations.
Framework-mapped coverage and an audit trail created as the work happened.
A legacy application was decomposed and migrated in controlled releases, with parallel running used until interfaces, data and recovery paths were accepted.
A supportable application foundation introduced without forcing an unsafe cutover.
Request, incident and change journeys were simplified before configuration, then governed through a shared platform backlog and release cadence.
Clearer ownership, fewer workarounds and a platform the internal team could sustain.
Landing-zone controls, observability, cost ownership and recovery practices were treated as one operational design rather than four separate technical projects.
Teams could release into a governed environment with service health and spend visible together.
Roles were assembled around a defined platform work package, with delivery measures, knowledge transfer and a planned exit built into the engagement.
Specialist capacity added without creating a permanent dependency on individual contractors.
Anatomy of the work
The portfolio format is designed for buyers and delivery leaders evaluating relevance. It shows enough of the operating logic to understand whether the experience transfers to their environment.
Name the service, users, constraints, dependencies and decision owners before describing a solution.
Separate the operating-model, process, platform and people changes so the reader can see what did the work.
Record the sequence, decision gates and transition approach instead of collapsing months of work into a technology list.
State the baseline, measurement window and owner behind each claim, including the parts that remain qualitative.
Evidence standard
A polished result without provenance is still only marketing copy. The publishing workflow should make the status of every claim obvious to editors and readers.
Client identity, quotation and identifying context are published only at the level explicitly approved.
Aevis contribution is separated from client teams, other suppliers and changes already under way.
Numeric claims carry a baseline, period, source and named verification owner before they are made public.
Constraints, dependencies and responsibilities are stated so the result is not presented as universal or guaranteed.
Capability coverage
Many engagements begin in one service line and create value across several. The portfolio keeps those relationships visible without pretending every project is an end-to-end transformation.
Operate and improve
Design the flow of work
Protect and evidence
Modernise and connect
Add accountable capability
Portfolio questions
Clear proof needs clear limits.
Enterprise technology work often exposes sensitive operating, security or supplier information. A study is anonymised when naming permission is not available or when the useful lesson does not require the client identity.
No. Outcomes depend on the starting environment, scope, adoption, client decisions and third-party dependencies. A case study is evidence of relevant experience, not a universal performance commitment.
Where permissions allow, supporting architecture, delivery artefacts or references can be discussed under the appropriate confidentiality terms. Availability is confirmed case by case.
A publishable metric should identify its baseline, time window, source and verification owner.
Start with your context
Tell us the service, constraint or recurring problem you are working through. We will bring the most relevant experience into the first conversation and be clear about what is — and is not — comparable.