AIOps | Self-Healing Operations | BizOps | FinOps
Run Without Firefighting. Improve With Every Incident.
Most IT operations teams are caught in a loop: alert fires, war room convenes, repeat. The same incidents recur because the knowledge from resolving them never makes it back into the system.
QyrusAI Application Run AI breaks that loop – by correlating signals across your entire estate, resolving the routine without a human, and turning every production incident into permanent operational knowledge that prevents the next one.
Challenges
The Operations Problem
Every IT Team Knows.
Stop letting alert fatigue, recurring outages, and tribal knowledge loss overwhelm your IT operations. Discover how unified event intelligence and service-level cost attribution turn reactive firefighting into predictable, autonomous resilience.
Alert Noise Without Context
Thousands of alerts, dozens of tools, no single view of what actually matters. SRE teams spend more time triaging than fixing — and still miss the signals that matter.
Operational Knowledge Lives in People
Runbooks live in one engineer's head. When they go on leave — or leave the company — the knowledge goes with them. Attrition is an operational risk most teams have no plan for.
Repeat Incidents Recur Forever
Every post-mortem produces action items. Few of them reach the systems that generated the incident. Next month, the same war room convenes for the same issue.
Costs Are Attributed to Departments, Not Services
Cloud spend, application run costs, and license fees appear on an IT bill — but cannot be attributed to the specific business services or applications that generated them. Finance and IT are always negotiating from different data.
How it works
See Everything. Resolve Automatically. Learn Permanently.
Here is how Qyrus Application Run AI addresses each of these — by sitting above your existing monitoring stack, correlating signals across the whole estate, and building permanent operational knowledge with every incident.
No rip-and-replace of existing tools
Qyrus AIOps ingests signals directly from Dynatrace, Datadog, New Relic, Splunk, and ServiceNow — whatever your teams already run. There’s no rip-and-replace project and no new monitoring stack to roll out. Existing investments stay in place; what changes is what happens to the signals once they arrive.
Infrastructure, application, and business layers
Signals get correlated across infrastructure, application, and business service layers simultaneously, not analyzed one tool at a time. This is how thousands of individual alerts become a handful of grouped incidents, each tied to its actual root cause rather than its alert volume. Teams stop chasing noise and start seeing the one thing that’s actually broken.
Recurring incidents surface before anyone asks
When an incident matches a recurring pattern, it’s flagged automatically instead of waiting for someone in a war room to ask “have we seen this before?” That question gets answered before it’s even asked. Recurring patterns also become the evidence base for deciding what actually needs fixing for good, not just patching again.
Every incident tied to business cost
Every incident is annotated with the business service it affects, the dependency path that caused it, and the operational cost of the disruption — pulled from the same Knowledge Graph that powers Modernize. This turns “server CPU spike” into “checkout service degraded, revenue at risk.” Prioritization stops being a guess about which alert looks scariest and starts being a fact about what actually costs the business money.
Highest-volume tickets handled without humans
L1/L2 automation takes on the ticket categories that consume the most on-call hours — restart sequences, threshold responses, known incident patterns — and resolves them within policy bounds. This is where the bulk of routine operational effort typically lives, and where automation delivers the largest immediate relief. Engineers get their nights and weekends back for the work that actually needs a human.
Governed remediation within your autonomy level
Self-healing operations close failure patterns before they repeat, executing within whatever autonomy level you’ve configured for that action class — Level 0 through Level 3. Nothing acts beyond the boundary you’ve set for it. The system gets more capable of preventing the next incident, not just responding faster to this one.
Timestamp, policy, confidence, and outcome recorded
Every automated action is logged to Qhive with a timestamp, the policy reference it acted under, a confidence score, and the outcome. Nothing runs silently — the automation is exactly as auditable as the process it replaced, if not more so. This record is what makes it possible to trust automation in regulated environments, not just fast ones.
Resolution time cut from hours
A traditional workflow — manual ticket creation, routing, multi-level support — typically consumes 1 to 10 hours before an incident is even diagnosed. QyrusAI’s Auto-Resolve compresses that same diagnosis-and-fix cycle to under 30 minutes. That’s not a faster version of the old process — it’s a different process entirely, one where the fix is often already validated before a human would have finished reading the ticket.
Never a binary on/off decision
The autonomy framework runs on a Level 0–3 model — Level 0 recommends only, Level 1 prepares artifacts, Level 2 acts in controlled environments, Level 3 acts in production with explicit human sign-off. You set the level per action class, not per system or blanket policy. AI in production is never a binary switch — it’s a dial you control action by action.
Recommendation, confidence, approval, and outcome
Every action, at every autonomy level, is evidence-logged — what was recommended, at what confidence, who approved it, and what happened next. That trail exists whether the action was a Level 0 recommendation a human overrode or a Level 3 remediation that ran in production. If someone asks what the system decided and why, the answer already exists.
DORA, FCA/PRA, and NIST built in
DORA, FCA/PRA, and NIST AI RMF governance posture is met by the platform’s architecture itself, not by a compliance layer bolted on afterward. Regulated environments run at Level 2 or Level 3 by default, with the evidence trail regulators actually ask for already generated as a byproduct of normal operation. Compliance stops being a separate project and becomes a property of how the system works.
Every high-risk step reviewed by design
Agents operate under least-privilege access, and every decision is scored for accuracy and relevance before it’s acted on. High-risk steps always carry a human-in-the-loop approval gate — autonomy doesn’t mean unsupervised, it means responsibly bounded. This is what lets a C-suite trust agentic decisions without having to review every single one of them personally.
Cost attributed to apps, not centres
Full cloud cost visibility gets attributed to the applications and business services actually generating it, not just parked against generic cost centres. Cloud spend, license utilisation, and idle resource waste all show up per application and per business service. Finance and IT finally look at the same number and mean the same thing by it.
Business impact shown, not IT metrics
BizOps dashboards link IT health directly to business service performance, in real time. A COO doesn’t need to interpret server metrics — they see failed transactions, delayed processes, and revenue-impacting downtime in business language. IT stops reporting uptime and starts reporting outcomes the business actually cares about.
Recurring incidents become modernization priority signals
Recurring incident patterns get scored as modernization priority signals directly in the Knowledge Graph, feeding straight into the Prioritize stage on the Modernize side. The applications generating the most operational pain automatically surface as the applications most worth fixing for good. Modernization stops being a separate initiative pitched from scratch and becomes the natural next step the evidence already points to.
Licenses reclaimed based on real usage
The same continuous discovery that powers everything else on this platform also tracks what software is actually being used versus what’s simply being paid for. Unused and underutilized licenses get identified and reclaimed instead of silently renewing every year on assumption. Organizations taking this approach typically see a 30–40% reduction in software costs, with far less manual reconciliation to get there.
Core Features
Unify event intelligence, cloud cost management, automated remediation, and digital twin simulation across your entire enterprise estate. Discover how end-to-end operational visibility turns complex IT environments into autonomous, self-healing systems.
Built for Operations Teams
That Run Complex Enterprise Estates at Scale.
AIOps & Event Intelligence
Correlates signals across your full estate — application, infrastructure, and business service. Noise reduction from thousands of alerts to actionable incidents.
BizOps
Business service health in real time — maps IT events to the business services and transactions they affect. What the COO sees, not just what the dashboard shows.
FinOps & Cloud Cost Intelligence
Full cloud and application spend visibility attributed to business services. Idle resource detection, license optimisation, and vendor overlap identification.
DevSecOps
Security embedded in the delivery pipeline — vulnerability detection, shift-left security testing, and continuous compliance monitoring without slowing release velocity.
Self-Healing Operations
Governed remediation for known failure patterns — resolves recurring incidents autonomously within configured policy bounds. Every action logged, reversible, and auditable.
L1/L2 Automation
Agentic automation for the ticket categories that consume the most on-call hours — restart sequences, threshold responses, runbook execution — without human triage.
Intelligent Twin
A digital twin of your production estate — simulates the impact of any operational change before it runs. Change management risk reduced to a pre-approved blast-radius score
Shadow IT Discovery
Continuously discovers applications and SaaS tools procured outside IT governance — including AI tools. Closes the gap between the CMDB and reality.
Software Asset Management
Full software license inventory with utilisation analytics per application and per team. Renewal preparation from evidence, not from what the vendor tells you.
A GLIMPSE ON THE NUMBERS
Fewer Incidents. Faster Resolution. Autonomous Operations. Evidence-Backed.
Cut incident resolution times in half, slash reactive tickets by 30%, and drive down run costs. Discover how autonomous operations deliver a proven 200% ROI across complex enterprise environments.
Faster incident resolution
Fewer reactive incidents
Run cost reduction
ROI — UK bank, full platform
Continuous testing. Continuous learning. Continuous value.
Join leading enterprises
driving the loop forward.
Integrations
Sits on Top of Your Existing Monitoring Stack No Rip-and-Replace Required
QyrusAI reads from your existing observability tools and writes context back to them — so your current monitoring investment is enriched, not replaced. The intelligence layer runs above Dynatrace, Datadog, and ServiceNow; the stack you built does not change.
CUSTOMER TESTIMONIALS
Enterprises running the loop
in their own words.
Real Teams. Real Outcomes. Real Confidence in What Ships Next.
Discover how teams worldwide are leveraging QyrusAI.
Qyrus not only supports the testing of our Web, Mobile, and API components as part of our CI/CD processes, but also in ongoing regression-testing across our partner ecosystem. The real power of Qyrus is that we have this extremely broad testing capability in one tool, run in the cloud, and reusable across all our development teams.
The transition to Qyrus has marked a significant turning point for us. By embracing automation, we not only streamlined our SAP test automation process but also achieved a remarkable reduction in overall project testing time by 40%.
... the Qyrus platform have been a great addition to Monument's product delivery capabilities. Within a few months, we have been able to create a comprehensive test suite of complex end-to-end test scenarios spanning multiple platforms and channels. The Quinnox team has helped us embed the Qyrus solution, and the supporting processes around it, into our agile delivery methodology.
Resources
Explore our curated library of expert blog posts, in-depth whitepapers, and real-world case studies, designed to help you stay ahead in the world of AI-driven application lifecycle management.
Blog
October 15, 2026 | 
4 min
Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It
Read More
October 14, 2026 | 5 min
The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value
Read More
October 9, 2026 | 8 min
Application Discovery and Dependency Mapping: The First Step Before You Modernize Anything
Read More
September 24, 2026 | 6 min
How a Global Data & Analytics Leader Scaled SAP GUI and Fiori Test Automation for Complex BRIM Workflows
Read More
September 17, 2025 | 5 min
From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation
Read More
June 27, 2025 | 5 min
AI-Powered Testing Transforms One of the Largest Beverage Companies
Read More
Blog
October 15, 2026 | 4 min
Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It
Read More
October 14, 2026 | 5 min
The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value
Read More
October 9, 2026 | 8 min
Application Discovery and Dependency Mapping: The First Step Before You Modernize Anything
Read More
Case Study
September 24, 2026 | 6 min
How a Global Data & Analytics Leader Scaled SAP GUI and Fiori Test Automation for Complex BRIM Workflows
Read More
September 17, 2025 | 5 min
From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation
Read More
June 27, 2025 | 5 min
AI-Powered Testing Transforms One of the Largest Beverage Companies
Read More
Reports
Whitepaper
FREQUENTLY ASKED QUESTIONS
Common Questions About
QyrusAI Operate
How is this different from Dynatrace, Datadog, or our existing monitoring tools?
Dynatrace and Datadog produce excellent telemetry. QyrusAI Operate does two things they do not: (1) it takes automated action, not just detection — L1/L2 automation resolves the routine ticket without a human; (2) it correlates to business impact, not just infrastructure metrics — BizOps maps what the IT event means for a specific business service in financial terms. We sit above your existing monitoring stack, not instead of it. The conversation is never “replace Dynatrace” — it is “what happens after Dynatrace fires the alert?
Does it require agent installation on every system?
No. Operate reads from APIs, event streams, and connectors into your existing monitoring and ITSM tools. For some environments a lightweight collector is deployed for specific data sources, but this is scoped per engagement. You do not need to instrument every system before value is delivered.
How does the self-healing and L1/L2 automation work — and how is it governed in a regulated environment?
The autonomy framework uses a Level 0–3 model: Level 0 recommends and a human decides; Level 1 prepares the artifact; Level 2 acts in a controlled environment; Level 3 acts in production with explicit human approval required. Regulated environments — financial services, healthcare, public sector — are configured at Level 2 or Level 3 by default. Every automated action is logged to Qhive with timestamp, policy reference, confidence score, and outcome. The audit trail exists before a regulator asks for it.
What is the blast-radius mapping capability and how does it differ from a CMDB?
A CMDB records what someone entered. Blast-radius mapping in Operate is derived from what the system actually does — signals observed across the estate are used to infer which systems depend on each other and how much traffic flows between them. When a change is proposed, the blast radius is calculated from observed dependency data, not from a manually-maintained record. The gap between the CMDB and the observed reality is usually the first thing customers see when we run an estate assessment.
How does Operate connect to Modernize and Assure — is it a separate purchase?
Operate delivers standalone value — AIOps, L1/L2 automation, FinOps, and BizOps all work independently. But the joint value of the three pillars is significantly greater: recurring incidents from Operate become regression tests in Assure (closing the production feedback loop), and operational cost data from Operate feeds the Modernize prioritization engine (telling you which systems to transform first based on what they are actually costing you in production). The commercial model supports starting with one pillar and expanding — most customers start with Operate or Assure and add the others in sequence.
Does it support SAP environments?
Yes. Operate connects to SAP systems through the SAP connector. AIOps correlates SAP production events — IDoc failures, BAPI errors, transport import issues — to the affected business processes and surfaces them alongside non-SAP signals. Self-healing operations work in SAP environments where the failure pattern and remediation step are known and policy-approved. L1/L2 automation can handle SAP-specific runbook steps (transport import retry, IDoc reprocessing, job restart) within configured governance bounds.
• STAY AHEAD, STAY IN THE LOOP •
Turn Operational Knowledge Into Autonomous Action
Starting With One Incident Tower.
30-minute walkthrough — we will show you what noise reduction and L1/L2 automation look like on your highest-volume incident tower, with a cost-per-ticket trajectory.
