- AMS Lifter
Every Incident Starts From Zero. The Business Pays for It by the Hour.
AMS Lifter, powered by agentic AI, builds one living record of your estate and incident history, then earns the right to act by proving itself against your own incidents first. Autonomy grows with evidence, backed by a full audit trail.
Built for application management and platform engineering leaders deciding where an AI-led AMS actually pays off, and where to start.
Every Lost Hour is Business You Don't Get Back
$2M / hour
median cost of a single high-impact outage
77 hours
of high-impact outage downtime a year (median), with engineering teams spending 30% of their time on disruptions
4x
the cost of a ticket escalated from L1 to L2 versus one closed at L1 ($84 vs $22), with handle time rising at every tier
Add it up and unplanned downtime now costs the average Global 2000 company $300M a year, up 50% in two years (Splunk / Oxford Economics, The Hidden Costs of Downtime, 2026). The same failures keep coming back, and every escalation starts cold: slower to resolve, and paid for again at a more expensive tier.
Knowledge Debt Costs You Twice: In Hours and In Dollars
Right-shoring, automation, and deflection all squeezed tiers and tickets.
None of them touched the understanding that gets rebuilt on every incident and thrown away at every handoff.
So every incident is slower than it should be and more expensive than it needs to be, and the run budget keeps paying to rediscover the same fix.
Ticket Raised
clock starts
L1 Intake
triage & attempt
Escalate L2
cold & re-discovers L1
Escalate L3
cold & re-discovers L2
Resolution
context lost every hop
- Context is lost at every handoff
- Every escalation burns business-critical time
- Fixes live in people's heads, not the system
- Measured on tickets closed, not outages prevented
01
Right-Shoring
Moved who does the work.
02
Automation
Sped up the tickets.
03
Deflection
Reduced what reaches a person.
Knowledge debt: untouched by all three.
The Next Tier Starts with the Ticket Number
Traditional AMS optimizes how incidents are handled. It does not necessarily preserve what each incident teaches the organization. AMS Lifter treats that knowledge as an operational asset.
Builds a governed context graph from the estate, connects that context to incident history, and carries it into the next diagnosis and the next handoff.
Traditional AMS
Context is reconstructed during incidents.
Handoffs can lose diagnosis context.
Knowledge sits across tools and people.
Automation is applied to individual workflows.
The AMS Lifter approach
A living context graph carries the estate's history into each incident.
Findings, dependencies, prior fixes, and ruled-out paths travel with the record.
Relationships become queryable in one governed operational context.
AI capabilities work from enterprise context and historical evidence.
Two Things Must Come Together
Together they pay down the knowledge debt. Either one alone fails. The payoff is speed and cost together.
Faster time to diagnosis, fewer escalations that start cold, and a lower cost per incident.
01
Memory
One living record built from the enterprise's own history - tickets, code, configuration, conversations. So nobody rebuilds the picture from scratch on every incident.
02
The right to act
Every capability is replayed against your own incident history before live access, with autonomy expanding only when the evidence justifies it.
Why both?
Memory without scoring gives you faster mistakes. Scoring without memory has nothing to work from.
25–40% Realistic MTTR Improvement
Published AIOps benchmarks indicate 25–40% MTTR improvement within 12–18 months once the required memory and scoring foundation exists. At $2M for every hour of a high-impact outage, that improvement is measured in business hours recovered, not tickets closed.
One Record Across Five Stages
01
Triage
Locate the event in the estate and surface the context that matters.
02
Diagnosis
Correlate the incident with recent changes, dependencies, and prior incidents.
03
Business impact
Connect the technical event to the funds, portfolios, reports, or processes it may affect.
04
Remediation
Propose approved resolution options within the defined scope and permissions.
05
Escalation
Carry findings, dependencies, prior fixes, and ruled-out paths to the next engineer.
Nobody restarts cold.
Keep the Context Inside Your Boundary
Data handling
The graph is built from your enterprise data and remains within your boundary.
Isolation
Each engagement has its own versioned knowledge core, access controls, and residency.
Read-first access
Initial connectors are read-first across ITSM, observability, code, and CMDB systems.
Auditability
Capabilities and actions remain reviewable, with high-risk actions subject to explicit human approval.
Start Small. Prove it Against Your Estate.
Indium combines AMS Lifter with engineering judgement, readiness assessment, governance, and continuous evaluation. One continuous service that scales with the estate under management.
Readiness assessment
Assess the selected estate, data sources, operating model, and baseline.
A scope grounded in measurable need.
Context build
Connect the agreed sources and rebuild the operational context.
A living record of the estate and its history.
Replay and evaluation
Run capabilities against historical incidents before live access.
A result you can check against your own evidence.
Controlled deployment
Set the boundaries for human approval and autonomous action, then expand only when justified.
A safer path from assistance to autonomy.
An Indium engineer stays involved where judgement, validation, and governance matter. You are not handed a black box and asked to trust it.
Pick the Part of the Estate You Want to Prove
Run a readiness assessment against one estate, not the whole portfolio.
What you walk away with
- A readiness assessment and defined operational baseline
- A governed context built from the selected estate and incident history
- Capability results replayed against your own historical incidents
- Clear boundaries for what AI may recommend, draft, or act on
- A go or no-go decision backed by evidence before scale
Frequently Asked Questions About AMS Lifter
AMS Lifter is Indium’s approach to AI-led application management built around two foundations: a living operational context of the enterprise estate and evidence-based controls for how AI capabilities are allowed to act. It helps teams use their own incident history, technical dependencies, business context, and operational knowledge to improve diagnosis, resolution, prevention, and recovery.
The graph connects technical assets and dependencies, incident and ticket history, runbooks, architecture, KEDB records, business context and process details, and ownership. It is designed to make the relationships between those elements queryable, with provenance-tracked so teams can understand where the context came from.
No. Documentation can be useful, but AMS Lifter’s context graph is also populated through estate archaeology — reading the estate itself to derive code-level dependencies, business process maps, rules embedded in code, and incident-to-code relationships. This reduces dependence on documentation that may have drifted away from the system.
Capabilities are replayed against your own historical incident data before live access is granted. Performance is scored against evidence; high-risk actions remain behind explicit human approval, and autonomy expands only as evidence accumulates. The model is designed for earned autonomy rather than a one-time switch.
The proposed starting point is the Inbound Adaptor and Data Fabric modules within the IM platform. The initial engagement uses read-only access to relevant ticketing, databases, monitoring logs, KEDB, APIs, and CR details, establishes an agreed availability and recovery baseline, and defines what requires human approval before any write-back or live autonomy is considered.
Opex. AMS Lifter is delivered as a managed, run-the-business service rather than a platform you build and capitalize. You start with one estate and a 4-week Proof of Value, pay for the scope under management, and expand only when the evidence justifies it, so the spend sits in the operating budget you already use to run applications, and it is tied to results you can measure.