Capabilities / AI & Intelligent Automation
Enterprise AI that works inside the operating model.
Aevis identifies, builds and operates AI use cases that reduce manual effort, improve decision context and coordinate action across enterprise systems — while data, permissions, approvals and accountability remain governed.
- 07services this capability runs across
- 04ways to start, from assessment to managed operations
- Agreedaccuracy criteria before anything reaches production
Where we apply it
A capability across every service — not a service of its own.
AI is not something Aevis sells beside its services. It is applied inside them, against work those services already own, and it is measured by whether that work got better.
IT Service Management
AI-assisted intake, resolution, knowledge and workflow orchestration.
- Summarise and classify incoming work
- Suggest probable resolution with its evidence
- Draft knowledge against real demand
End User Computing
Conversational support, experience intelligence and controlled self-healing.
- Natural-language employee assistance
- Detect experience degradation before contact
- Execute approved runbooks, log every action
Staffing Solutions
AI-ready technology talent, supported by human technical validation.
- AI, ML and automation specialists
- Assisted skills discovery and matching
- Every hiring decision made by a person
Managed Services
AIOps correlation, probable-cause insight and governed remediation.
- Compress related alerts into one incident
- Rank probable cause from change and telemetry
- Automate only what policy already approves
Cybersecurity
AI-assisted enrichment, investigation and risk-based prioritisation.
- Enrich alerts with asset and identity context
- Draft the investigation timeline for review
- Response stays analyst-approved
Software Solutions
Enterprise copilots, knowledge assistants and governed workflow agents.
- Retrieval applications over approved content
- Agents bounded by policy and approval gates
- Evaluation and observability built in
Corporate Training
AI and data literacy taught, and assisted authoring behind the material.
- Verification habits taught alongside the tooling
- Material tailored to one estate rather than reused generically
- No AI assessment of a named person, without exception
What we build
From the first assessment to a system you can run.
Seven areas of work. Most engagements use three or four of them, and which three depends entirely on what the assessment found — not on what was easiest to build.
AI opportunity assessment
Process, value, data and risk analysis across a defined scope, ending in a prioritised list rather than a strategy document.
- Process and volume analysis
- Value, feasibility and risk scoring
- Data and knowledge readiness
- Prioritised use-case backlog
Enterprise copilots and knowledge assistants
Assistants grounded in approved internal knowledge, answering within the permissions of the person asking.
- Permission-trimmed retrieval
- Source-linked answers
- Escalation with context retained
- Adoption measured by scenario
Agentic workflow automation
Multi-step execution bounded by policy, with approval gates and rollback designed in from the start.
- Task and tool definition
- Policy-constrained actions
- Human approval gates
- Exception routing and rollback
AIOps and intelligent operations
Correlation, probable cause and predictive capacity across the monitoring an estate already produces.
- Event correlation and noise reduction
- Probable-cause ranking with evidence
- Predictive capacity and cost anomalies
- Runbook recommendation
AI-enabled security operations
Enrichment, investigation summaries and business-risk prioritisation, with response left to an analyst.
- Alert enrichment and triage support
- Investigation timeline drafting
- Attack-path and exposure context
- Analyst-approved response only
Data and knowledge readiness
The work that decides whether any of the above can be trusted: what content exists, who may see it, and whether it is current.
- Content and knowledge inventory
- Permission and oversharing review
- Labelling, retention and residency
- Retrieval and indexing design
AI architecture and platform integration
Wiring model, retrieval and orchestration services into the identity, network and system-of-record architecture already in place.
- Platform and model selection
- Identity and network design
- Integration with systems of record
- Cost and capacity planning
Ways to start
Four ways in, and none of them is a twelve-month programme.
The right first step depends on how much is already known. An organisation with a named problem and a data owner starts differently from one that has been asked to “do something with AI”.
No agreed starting point yet
AI Opportunity Assessment
A time-boxed analysis of process, value, data and risk across an agreed scope, ending in a prioritised backlog with a recommended first use case.
Prioritised use-case backlog
One use case worth testing properly
Proof of Value
A single use case built against real data and evaluated against criteria agreed at the start, so the decision to proceed is made on evidence rather than on a demonstration.
Evaluated prototype and a go / no-go
A validated use case to build for real
Production Implementation
The full build — integration, permissions, evaluation, observability and guardrails — handed over with the run-books needed to operate it.
Production system with handover
Something already live that needs owning
Managed AI Operations
Ongoing operation, evaluation and improvement on an agreed cadence, under the same operating model as any other managed service.
Owned, monitored and reviewed service
Frequently asked questions
The questions worth asking before anything is built.
If a supplier cannot answer these plainly, that is the answer.
Is our data used to train models?
No. Content submitted through a system Aevis builds or operates for you is used to answer the question in front of it and is not used to train a model. Where a use case would require anything else, we say so before it is built and it becomes your decision, not an assumption in a contract.
What does AI actually decide?
On its own, nothing with a consequence. Every use case names its human-control point before it is built: AI classifies, summarises, correlates, recommends and — inside a policy that has been approved — executes routine work. Risk acceptance, service ownership, hiring decisions, customer commitments and anything legal or financial stay with a person.
How do we know it is accurate enough?
Accuracy criteria are agreed before release, tested against a set built from your real cases, and re-run on every change. A capability that does not meet its criteria does not go live, and one that drifts below them in production raises an alert rather than waiting for somebody to notice.
Do we need to buy new platforms?
Usually not. We work with what you have already chosen and licensed, and a large proportion of what the brief calls AI is native capability in ServiceNow, Microsoft 365, Azure or Salesforce that is not yet switched on or not yet safe to switch on. We do not introduce a separate AI layer where the platform in place is sufficient.
Can we start with one small thing?
That is the recommended way. A Proof of Value takes one use case, builds it against real data and evaluates it against criteria set at the start. It is designed to produce a defensible go or no-go, including a no-go — a proof of value that cannot fail is a demonstration.
We already built something. Can you take it on?
Yes. That usually begins with an assessment of what exists — its data access, evaluation history, cost and failure modes — because taking operational ownership of a system nobody has evaluated is how an AI capability becomes an incident.
How will we know it was worth it?
A baseline is taken before the work starts, and the same measure is reported afterwards on a fixed cycle. We report human intervention and rollback rate alongside the improvement, because a capability people quietly work around is not a saving.
AI enquiry
Start with the work, not the technology.
Tell us what is taking too long, costing too much or depending on one person’s memory. That is a better opening than a product name.
- Response
- One working day, Monday to Friday