Agentic Run

27%

Fewer false alert signals

70%

Auto-resolution without human touch

28%

Average OpEx reduction

<10s

Agent reasoning latency per action

Why the industry hasn't fixed this yet

Two problems, not one — and nobody in the old model is rewarded for fixing either.

Add AI tooling to a headcount-and-server business model and the tooling makes the analyst faster — a copilot drafts the response, auto-fills the ticket — but nothing about it reduces how many tickets get created in the first place. You get faster tickets, not fewer of them.

And the usual fix — a multi-year transformation program with compounding savings promised for year three — rarely survives year two. Budgets get cut, staff rotate, scope shrinks, and the "compounding" turns out to have been compounding on paper, not on the invoice.

We built something different: a nervous system for your estate. Not a platform, and not a three-year bet — a sensing and response system that gets deployed incrementally, domain by domain, proving itself in weeks.

Network degradation caught before anyone complains.

A provider SLA measures uptime, not whether traffic is actually routing efficiently or whether a segment is quietly degrading. We instrument the same signal a network provider already has, made visible to you in real time.

Security at scale — a reflex, not a ticket.

A vulnerability normally sits in a queue for weeks while a security team triages by hand, one CVE at a time. Here, a patch or a policy gets proposed and validated the moment the vulnerability is sensed.

A CMDB that stays correct — automatically.

Most CMDBs are wrong the day after they're built. This is where memory starts: the CMDB stops being a document someone updates from memory and becomes a living record the system corrects every time it senses something it didn't already know.

The shift

Reactive → Proactive → Adaptive → Intelligent. One loop. All eight domains.

Not eight different transformations — one operating loop, moving each domain through the same four-stage journey on its own timeline.

Where most start

Reactive.

A human notices something's wrong before anything happens. Eight domains, eight tools that don't talk. The billing model wants you to stay here.

Headcount

Unit of value

Sensing before escalation

Proactive.

The system senses the signal before a user does. One substrate, all eight domains. Signal correlated, incidents caught early.

27%

Fewer false alert signals

Adjusting in real time

Adaptive.

The system acts, governed by risk. One governance model, all eight domains. Governed actions taken automatically.

70%

Auto-resolution rate

Smarter every resolution

Intelligent.

The system improves itself from every outcome. One feedback loop, all eight domains. Compounding accuracy — better, not just faster.

28%

Average OpEx reduction

One operating loop. Eight domains.

Things you run (5)

●

Cleaner signal

●

Infrastructure & Cloud

●

Digital Workplace

●

Application Support

●

Managed Security

Governance — runs across all five (3)

Service Desk

Service Management (ITSM)

FinOps

Reactive

(where most start)

Proactive

Adaptive

Intelligent

Trigger

A person notices, then opens a ticket

The system senses the signal before a user does

The system acts, governed by risk

The system improves itself from every outcome

Unit of value

Headcount, server count, ticket volume

Signal correlated, incidents caught early

Governed actions taken automatically

Compounding accuracy — the system gets better, not just faster

Coverage

Eight domains, eight tools that don't talk

One substrate, all eight domains

One governance model, all eight domains

One feedback loop, all eight domains

Scaling

Add more people

Add more sensing

Add more governed autonomy, exactly where it's earned

Add more history — it compounds on its own

Stage one · where most organizations are today

Reactive.

A human has to notice something's wrong before anything happens — a user reports it, an alert fires and sits in a queue until someone gets to it, or a monthly review surfaces a trend three weeks late.

Network metrics live in one system, infrastructure metrics in another, application traces in a third, security alerts in a fourth, cloud cost in a fifth, service desk tickets in a sixth, endpoint status in a seventh, and the CMDB — if it's kept current at all — in an eighth. Every tool promised to be the one pane of glass; most estates end up with nine.

This is the stage the billing model wants you to stay in. Every ticket is revenue for someone; nobody's incentivised to help you leave it.

Today's reality

Eight contracts. Eight tool sets. Eight teams that hand off to each other by ticket. No single view of the actual business transaction end to end.

The three approaches that don't work: 1. Traditional MSP — sells you people watching dashboards. 2. Point-tool vendor — sells you a dashboard. 3. Proprietary AI platform — sells you a walled garden with a subscription attached. None leaves you with an architecture you actually own.

tage two · sensing before escalation

Proactive.

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Sense + Reason

"Nerve endings don't wait for the brain's permission to register pain — the signal exists the instant the condition does."

Tomorrow

An incident gets diagnosed once, correctly — often before a user notices, and never bounced between teams each proving it isn't theirs.

What we engineer

Unified signal ingestion across all eight domains — network telemetry, infrastructure and application telemetry, security events, cost data, service desk tickets, endpoint state, and CMDB records — normalised into one substrate.

Agentic correlation turns noisy signals into a small number of real incidents. Root-cause reasoning spans network, infrastructure, application, security, and change data in the same pass — a service-mapped CMDB, kept current automatically, is what makes "what does this actually touch" an answered question instead of a Slack thread.

In practice that means enriching the ServiceNow instance you already run — the same Incident, Problem, and Change modules, now checked against a CMDB that's actually current, not a second system competing with it.

Result

27%

Fewer false alert signals

Technology

ServiceNow enrichment · Agentic correlation engine · Unified CMDB · Real-time signal substrate across all 8 domains

Already sensing — want to act on it?

Most teams get stuck at Proactive. Stage 3 is where cost actually comes out.

Book an assessment

Stage three · adjusting in real time

Adaptive.

Powered by:

Act + Govern

"A reflex isn't reasoning done in the moment — it's a decision already made in advance and wired in, so when the trigger happens, the body doesn't deliberate, it just acts."

Tomorrow

Known failure modes and known waste get resolved before a human ever sees the ticket. Autonomy scales exactly as far as it's earned the trust to scale — measurably.

Governed autonomous action across every domain — auto-scaling and failover in infrastructure, rollback and config correction in applications, auto-quarantine and patch deployment in security, self-service resolution for access requests and endpoint issues, automatic rightsizing in cost management.

We don't ask you to replace ServiceNow, your SIEM, or your existing FinOps platform. We make them trustworthy — enriched, current, and actionable — not replaced.

70%

Auto-resolution rate

70%

Faster MTTR

The Class 0–4 Autonomy Tier System

Every action gets classified into one of five auditable tiers before it runs. Not a vague risk score — a specific, logged tier assigned in advance.

C0

Observe Only

Sense and log. No action taken.

e.g. Metric logging

C1

Recommend

Propose the action to a human with full reasoning attached.

e.g. CVE patch suggestion

C2

Act with Approval

Ready to execute, awaiting one-click human OK.

e.g. Firewall rule change

C3

Act, Notify

Executes automatically; humans notified with audit trail.

e.g. Auto-scaling trigger

C4

Fully Autonomous

Runs unattended, audited. Reserved for proven blast-radius actions only.

e.g. Password reset

Want to see which actions on your estate qualify for each tier?

We score your estate before we propose any autonomy tier. No assumptions.

Book an assessment

Stage four · getting smarter with every resolution

Intelligent.

Powered by:

Learn

"A nervous system doesn't just react — it remembers, which is the entire mechanism behind muscle memory: the same reflex, executed with less conscious effort every time it's needed again."

Tomorrow

The number of things that break in the first place goes down, month over month — not just the time it takes to fix the ones that still do. The map of what you actually run stays accurate because it's a byproduct of running it, not a separate chore.

Every resolved action — human or agent, in any of the eight domains — feeds back into the loop's own knowledge: better correlation next time, a new candidate for automated remediation, a refined risk score, a CMDB entry that updates itself because the system that just acted on it is the same one that keeps it current.

28%

Average OpEx reduction

99.9%

Availability delivered

Beyond the incident

Reflexes and memory don't stop at outages.

The same nervous system runs through Change, Release, Incident, and Problem management — the ITIL processes every IT organisation already runs, whether or not anything is actively on fire.

Change management

Risk scored, not argued.

Risk no longer gets argued case by case in a CAB meeting — it's scored against the same dependency graph the loop runs on, so known-low-risk changes auto-approve and everything else arrives at CAB with the assessment already attached.

90% reduction in standard-change approval time · 99% first-time success rate

Release management

Conflicts caught before the calendar.

Conflicting releases get caught against the same dependency map before they're ever scheduled, not after they collide in production — every release carries its own risk-scored go/no-go instead of a gut check from whoever's on call.

65% reduction in release-planning effort · zero scheduling clashes

Incident management

Severity means the same thing.

Severity gets classified against the same risk model that scores every other action in the loop, so a P1 means the same thing regardless of who logged it — and every incident closes with a record detailed enough for Problem Management to actually use.

65% reduction in incident volume · 70% faster mean-time-to-detect

Problem management

Known errors stay fixed.

A root-cause fix becomes a tracked change candidate automatically, not a note buried in a closed ticket — so a known error stays fixed instead of recurring six months later.

55% reduction in repeat incidents · 3× faster problem closure

Ready to move from Reactive to Intelligent?

We deploy incrementally — each stage proving itself before the next one starts.

Book an operations assessment

Engineered, not automated

The architecture stays yours. That's the point.

ALTi AIOS isn't a platform you subscribe to. It's what we build directly into your estate, so the agentic ecosystem is yours to keep — not ours to keep renting to you.

Discipline 1

Semantic-layer graph engineering

1

Builds the dependency and ownership map that powers Stage 2's correlation and Stage 4's self-updating CMDB. This is what makes "what does this actually touch" an answered question instead of a Slack thread.

Discipline 2

Agent graph engineering

2

Why Sense, Reason, and Act can run as specialised agents in parallel — one handling infrastructure signal, another application signal, another remediation — instead of one generalist bottlenecking the whole loop.

"Traditional managed services scales by adding headcount. We scale by adding judgment exactly where it's still needed — and removing the need for it everywhere else."

What we build stays yours — on open standards, running on your estate, yours to keep the day the engagement ends.

Proof across the journey

Six numbers. One chain.

These aren't six separate wins. They're one causal chain, because they come from one operating loop — not six point solutions stitched together.

Proactive

~27%

Fewer false alert signals

Cleaner signal is what makes fast, safe automatic action possible in the first place.

Adaptive

70%

Auto-resolution, no human touch

Consistent automatic action is what compounds into lower cost and higher uptime.

Adaptive

70%

Faster MTTR

The same governed action that resolves incidents also shortens how long they last.

Intelligent

28%

Average OpEx reduction

Fewer repeated incidents and less manual toil show up directly as cost.

Intelligent

99.9%

Availability delivered

The compounding result of the first four rows — not a separately-bought SLA.

All stages

<10s

Agent reasoning latency per action

The speed that makes "sense, then act" fast enough to matter in production.

●

Cleaner signal

→

●

Safe automatic action

→

●

Lower cost + higher uptime

→

●

Fewer repeat incidents

→

●

99.9% availability

Case study · delivered engagement

A payments processor running fragmented observability across legacy systems, microservices, and third-party services — no shared strategy across any of it.

The challenge

The challenge wasn't a lack of dashboards. It was that nothing tied them together. A broken checkout flow or a slow login page had to be diagnosed component by component, with no single view of the actual business transaction end to end.

Incident intake was scattered across chat, email, and shared mailboxes, so knowing an incident existed took longer than it should have. The CMDB held configuration data, but wasn't wired into the change-management system's risk modules — so change risk still got argued in the room, not read off a dependency graph.

What we built

An observability agent analysed the codebase and runtime traces directly and generated the centralised business-journey dashboard from what it found — the full payment flow, ten critical applications mapped end to end.

AI-generated runbooks now exist for each service, built specifically for that service's own failure modes. Automated incident triage runs without a human starting the process. Zero-touch, self-healing remediation for well-understood failure modes.

Hours → minutes

Root-cause diagnosis time

70%

Mean time to detect

−45%

Repeat incidents

Book an operations assessment Read more case studies

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