Ingestion nobody decided
Sources were added one at a time, each defensible on its own. Nothing ever removed one, and the volume that results is treated as a fact of nature rather than a set of reversible decisions.
Splunk, a Cisco company
Platform expertise / Splunk
The bill is not a licence decision. It is an architecture decision.
Splunk will index whatever you send it, at whatever fidelity you send it, for as long as you tell it to. Every one of those is a choice somebody made — usually quickly, often years ago, and almost never written down. Aevis makes those choices explicit and then makes them again on purpose.
Splunk is third-party software selected and licensed by the client from Splunk, a Cisco company. Aevis provides advisory, engineering, content and operational services around the client’s deployment.
Machine data layer
Sourced · shaped · retained · answered- Platform
- Splunk
- Starting point
- Data and cost review
- Commercial model
- Project, sprint, managed or co-managed
Platform fit
Everything is indexed and nobody can answer the question.
The common failure is not too little data. It is a deployment carrying years of accumulated sources at full fidelity, where the search that would answer today’s question is slow, expensive, or returns three incompatible versions of the same field.
The same field, three names
Parsing was done per source by whoever onboarded it. Correlating across sources means knowing which name each one used, so cross-source searching is a specialist skill rather than a capability.
Dashboards and alerts nobody owns
Hundreds of saved searches, most inherited, many firing into a channel nobody reads. Deleting one feels risky because nobody can say who depends on it.
Our role is to make the deployment answerable, affordable and owned in your environment — not to sell an Aevis software product.
Product landscape
Where Splunk carries the operational record.
We shape the engagement around the products and entitlements your organisation has licensed. Scope, capability and pricing model always depend on your licensing and deployment shape.
Core
Splunk Enterprise and Cloud
The index, the search language and the data model everything else on the platform rests on.
- Indexing and retention
- Search and SPL
- Data models and acceleration
Security
Enterprise Security
Detections, risk-based alerting and the investigation workflow a security team actually works inside.
- Correlation searches
- Risk-based alerting
- Notable event workflow
ITSI
IT Service Intelligence
Service-level health built from the components underneath it, where the service definition is the hard part.
- Service decomposition
- KPI design
- Episode review
Observe
Observability Cloud
Metrics, traces and real-user data for teams whose question is latency rather than log content.
- APM and tracing
- Infrastructure monitoring
- Synthetic and RUM
SOAR
SOAR and automation
Playbooks that act on a finding — worth having only where ownership and exception handling are already clear.
- Playbook design
- Case management
- Approval gates
Pipeline
Ingest pipeline and edge
Forwarders, ingest processing and routing — the layer where volume and cost are actually decided.
- Forwarder estate
- Ingest filtering and routing
- Tiered and archive storage
Aevis capabilities
From an index to an answerable record.
Engage us for a focused intervention or an end-to-end programme. We work within your licensing, data-protection and retention obligations.
Data, cost and value review
What is being ingested, what it costs, what actually gets searched, and which sources have never appeared in a query anybody ran.
- Source-by-source volume, cost and search-usage analysis
- Retention reviewed against the questions and obligations it serves
- The sources that could be filtered, sampled, routed or dropped
Data onboarding and normalisation
Parsed once, correctly, against a common model — so correlating across sources stops being a specialist skill.
- Source onboarding with documented parsing and field extraction
- Common Information Model conformance for cross-source search
- Index, sourcetype and retention design that survives growth
Detection and content engineering
Detections written against a stated threat and a stated data source, with false-positive behaviour understood before they go live.
- Detection design mapped to coverage and to owned response
- Risk-based alerting rather than volume-based notification
- Retirement of content that produces noise without decisions
Service and operational analytics
Service health defined from the business service down, so a dashboard means something to somebody outside the platform team.
- Service decomposition and KPI definition with the owning team
- Alerting tied to service consequence rather than component state
- Reporting produced from the platform, not assembled for the meeting
Platform engineering and upgrade
Indexer and search-head architecture, capacity, upgrades and the migration work between deployment shapes.
- Cluster architecture and capacity sized to real search behaviour
- Version upgrades and app compatibility assessment
- Migration between on-premise, cloud and hybrid shapes
Managed and co-managed operations
Running the platform, the content backlog and the cost position, or standing behind a team that does.
- Platform health, capacity and licence-position operations
- Content backlog governed rather than accumulated
- Cost reviewed as an operating measure, monthly rather than annually
AI and analytics in machine data
The model can rank the queue. It cannot own the finding.
Splunk carries machine-learning and assistive capability, and it is genuinely useful for the things statistics are good at. The line below is where its output stops being a suggestion and starts being a decision — and that line does not move.
Anomaly and outlier detection
Baselining behaviour per entity and surfacing what has moved, for an analyst to interpret against context the model does not have.
Risk-based alerting
Accumulating weak signals against an entity so a notable event is raised on a pattern rather than on a single noisy rule.
Assisted search and summarisation
Drafting SPL and summarising an investigation timeline faster, with the query and its results shown rather than hidden.
What stays human — without exception
No AI closes a finding, suppresses an alert or authorises a response action. Triage conclusions, containment decisions and the choice to retire a detection are analyst judgements made under your operating model, and remain the accountable decision of the person who made them. Generated SPL is reviewed before it is saved, because a wrong query that returns rows is more dangerous than one that errors.
How value is measured
- Ingested volume against volume actually searched
- Cost per answered question, tracked over time
- Detection precision and analyst time per notable
- Cross-source searches possible without specialist knowledge
- Analyst override of model-ranked priority
Entitlement and data
Which machine-learning and assistive capabilities are available depends on the client’s Splunk products, version and licensing, and on what the vendor ships in that release. Indexed data is processed for the agreed operational purpose only, under the client’s data-protection and retention obligations.
Connected architecture
Decide what deserves to be indexed.
The most valuable architecture decision on this platform is a subtraction. Every source that earns full-fidelity indexing should have a question behind it, and the ones that do not can be filtered, routed to cheaper storage, or left where they are.
Sources
Endpoints, network, cloud, applications, identity and the third parties that also emit into your estate.
Ingest and routing
Forwarders, filtering, sampling and the routing decision that sets both cost and what remains answerable.
Index and model
Indexes, sourcetypes, retention tiers, the common information model and the accelerations that make search affordable.
Consumption
Detections, dashboards, service health, investigation workflow and the exports other systems depend on.
Architecture boundaryAvailable features, ingest pricing model, retention tiers and app compatibility depend on the client’s Splunk products, deployment shape and licensing. We validate entitlement and capacity assumptions before committing to a design.
Delivery model
Understand the bill before redesigning the platform.
Cost work and capability work are the same work here, and doing them separately is how a rationalisation programme accidentally removes the source somebody needed.
Review
Establish ingest volume by source, cost attribution, retention obligations and which sources are genuinely searched.
Source-level cost and value baselineAgree
Decide what earns full-fidelity indexing, what is filtered or routed, what is archived, and the questions each decision must still answer.
Written ingest and retention policyBuild
Onboarding, normalisation, detection and service content built against that policy, with parsing documented.
Normalised sources and owned contentPilot
Run the changed pipeline alongside the current one until the searches that matter return the same answers.
Verified parity before cutoverOperate
Run the platform, the content backlog and the cost position as one governed cycle.
Governed operating cycleImprove
Retire content that produces no decisions, revisit sources against changed questions, and hold the cost position.
Smaller content estate, held cost line
Use cases
What organisations bring us.
Each of these is a normal starting point rather than a programme. We map the adjacent dependencies so a local fix does not create a hidden failure elsewhere.
A renewal that has become a board conversation
Ingest grew year on year and nobody can attribute it. The work is source-level attribution before any negotiation.
Designed outcomeCost attributed to sources and owners
More notables than the team can assess
Detection content accumulated without retirement. Precision matters more than coverage once the queue exceeds capacity.
Designed outcomeA queue the team can actually work
Searches that cannot cross sources
Per-source parsing means every cross-source question is a specialist task. Normalisation makes it an ordinary one.
Designed outcomeCross-source search without a specialist
A move to Splunk Cloud
A migration is the moment the accumulated decisions become visible and the only cheap moment to revisit them.
Designed outcomeA migration that leaves less behind
Dashboards nobody outside IT reads
Service health built from components rather than from the business service it is meant to describe.
Designed outcomeHealth an owner recognises
A retention obligation nobody has mapped
Retention set per index by habit rather than against the obligation and the question it serves.
Designed outcomeRetention traceable to its reason
Engagement shapes
Four ways to start.
Which one fits is usually a question about where accountability should sit rather than about budget.
Data and cost review
Best forA renewal you cannot explain
A bounded assessment of ingest, cost, retention and search usage, ending in a source-level list of options with a saving and a consequence against each.
Engineering project
Best forOnboarding, normalisation or migration
Defined scope with acceptance criteria — onboarding, CIM conformance, detection content or a migration — handed over with the parsing documented.
Managed operations
Best forNo standing platform team
Aevis operates the platform, the content backlog and the cost position to an agreed cadence, with the accountability boundary set out in the service agreement.
Co-managed and enablement
Best forA team that should own this
We work alongside your team and hand over deliberately, with content documented and train-the-trainer where the capability should stay with you.
Designed outcomes
Measure the answers, not the volume.
Baselines and targets are agreed per engagement. We do not import a vendor benchmark into your estate and call it a business case.
Cost attribution
Share of ingest volume attributable to a named source and owner.
Data utilisation
Indexed sources that appear in a search anybody ran, over a stated period.
Detection precision
Notables leading to an action, and analyst time spent per notable.
Answerability
Cross-source questions answerable without specialist knowledge of parsing.
What we do not promise
No provider can guarantee a licence saving, a detection outcome or immunity from a threat. What is contracted is the engineering, the operation and the improvement practice within an agreed scope; the organisation retains its risk decisions, its retention obligations and its commercial relationship with the vendor.
Governance
The four things that keep this affordable.
This platform degrades in one direction: more sources, more content, more cost, and no mechanism that removes any of it. These are the standing controls that supply the missing direction.
Source intake
A new source is admitted against a stated question, an owner and a retention decision, rather than because somebody could send it.
Content lifecycle
Detections and dashboards carry an owner and a review date; content that produces no decisions is retired rather than muted.
Cost as an operating measure
Ingest and cost are reviewed monthly against attribution, so a change is noticed while it is small and reversible.
Retention traceability
Every retention setting traces to an obligation or a question, so the platform can be defended and, where warranted, shortened.
Why Aevis
Platform expertise with an operator’s perspective.
We approach Splunk as a system somebody has to run to a budget after we leave. The work is designed to survive handover, a renewal and a change of platform owner.
We look for what to remove
The most valuable recommendation on this platform is usually a subtraction, and it is worth less revenue to us than the alternative. A review that only ever adds sources is not a review.
Parse once, correctly
Normalisation is unglamorous and it is what decides whether cross-source questions are ordinary or specialist. We do it properly at onboarding rather than compensating for it in every search afterwards.
We run security and service operations
The people designing your detections also work queues. Precision, false-positive cost and what an analyst can actually assess in a shift are argued about from experience.
No licence resale
Licences are contracted directly between you and the vendor. We hold no margin in your ingest volume, which is worth checking for in any competing proposal.
Relationship clarityAevis does not claim ownership of Splunk products and this page does not state or imply a certified partnership. Product names and trademarks belong to their respective owners.
Testimonials
In their words.
Each testimonial is tied to the service it refers to, so service pages can draw the relevant one automatically.
The change we noticed first was not technical. It was that there was finally one person to call, and that person already knew the history of the problem.
Placeholder NameHead of IT OperationsNorthvale BankManaged Services They rebuilt the service catalogue around how our teams actually work rather than how the platform was shipped. Adoption stopped being an argument.
Placeholder NameDirector, Service ManagementHalden InsuranceIT Service Management We had the security tooling before Aevis arrived. What we did not have was anybody turning what it produced into decisions.
Placeholder NameChief Information Security OfficerCerulean HealthCybersecurity
Frequently asked questions
Questions teams ask early.
The useful answers depend on your deployment and licensing. These are the principles we use before an assessment establishes the exact scope.
Are you a Splunk partner?
This page makes no partnership claim. Aevis provides advisory, engineering, content and operational services around a deployment the client licenses directly from the vendor. Where a formal partner relationship is relevant to a procurement, ask us and we will answer it precisely rather than by implication.
Can you reduce our licence cost?
Often, and the honest version is that we can tell you what each reduction would cost you in answerability. Filtering a source, sampling it, routing it to cheaper storage or dropping it are all available, and each closes off some questions. We present the options with the consequence attached rather than a headline saving, because a saving that removes the source your next investigation needed is not one.
Should we move off Splunk to something cheaper?
Sometimes, and we will say so where the evidence points that way. But most of the cost we are shown is architectural rather than a product-price problem, and it moves with you: an estate that ingests everything at full fidelity is expensive on any platform. Establish the ingest and retention position first — it is the input to that decision either way, and it is worth having before a negotiation.
How does this sit with Grafana or our observability tooling?
Comfortably, and usually better than running one tool for everything. Splunk is strong where the question is about log content and investigation; metrics-first questions about latency and saturation are frequently cheaper and faster elsewhere. What matters is deciding which questions live where, so the same data is not paid for twice.
Will you write our detections?
Yes, against a stated threat and a stated data source, with false-positive behaviour understood before anything goes live. We will also argue for retiring content: past a certain queue depth, precision matters more than coverage, because a detection nobody has capacity to assess is not a control.
Can our own team take this over?
That is the co-managed shape. Parsing and content are documented as they are written, the ingest policy is yours, and train-the-trainer is available through the Corporate Training practice. A supplier whose model depends on you not holding this capability is the wrong supplier for it.
Splunk enquiry
Start with the bill, or the question you cannot answer.
Tell us what you ingest, what it costs, and the last question somebody asked that the platform could not answer. Those two together are usually the whole brief.
- Response
- One working day, Monday to Friday