Application Discovery · Dependency Mapping · Legacy Modernization
Stop Guessing What Will Break.
Simulate It First.
Your IT estate is too complex to manage from static diagrams and tribal knowledge. QyrusAI Intelligent Twin builds a live, high-fidelity digital replica of your ecosystem — so every change, incident, and optimization is tested in a safe environment before it ever touches production.
How it works
Challenges
The Blind Spot Every
Enterprise IT Team Lives With.
Static CMDBs go stale the moment they’re published, and no one can predict how a change will ripple through a system they can’t fully see. Discover how replacing outdated documentation and tribal knowledge with a living digital twin turns operations from reactive firefighting into confident, simulated decision-making.
Systems Too Complex to Hold in Your Head
Modern IT estates span legacy and cloud-native stacks with dependencies no single team fully understands — so every change carries hidden risk.
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Heavy Dependency on Tribal Knowledge
Institutional knowledge lives in a handful of SMEs’ heads, not in the system – so response time and quality depend on who’s on call.
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Change Without a Safety Net
Deployments, migrations, and configuration changes are pushed into production with no way to validate impact beforehand – failures are discovered live.
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Resilience Testing That Risks the Thing It’s Testing
Chaos and failure testing against live production is too risky to run often — so most organizations under-test and get surprised by outages.
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Download PoV
How it works
Model the Estate. Simulate the Risk.
Predict the Impact. Power the Automation.
Explore how QyrusAI continuously models your IT ecosystem, runs safe-to-fail simulations against a living replica, and feeds that intelligence into every autonomous workflow across the platform – without ever risking production.
Model
Simulate
Predict
Power
Continuously model your IT ecosystem – automatically, in real time.
QyrusAI builds and maintains a high-fidelity digital twin of your hybrid IT estate — servers, VMs, applications, and their interdependencies — as they actually exist, not as they were last documented. No static CMDBs. No stale diagrams. The estate is modeled continuously, so the twin never drifts from reality.
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Test changes and failures in a safe environment – before they’re real.
Every deployment, configuration change, or failure scenario can be run against the twin instead of production. Chaos engineering and resilience experiments happen in a “safe-to-fail” space, so teams can stress-test system stability without risking a single live transaction.
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See how a change will ripple through the system before you make it.
The twin’s dependency model turns “what if we deploy this?” into a predictable answer. Impact prediction surfaces downstream conflicts and risks during Change Management, so validated changes replace guesswork.
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Feed every autonomous workflow with a live, trusted picture of the estate.
The Intelligent Twin is the perception layer behind Auto Discover, Detect, Diagnose, and Resolve — giving every agentic workflow across the platform the current, contextual state it needs to act correctly, not just quickly.
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Continuously model your IT ecosystem – automatically, in real time.
QyrusAI builds and maintains a high-fidelity digital twin of your hybrid IT estate — servers, VMs, applications, and their interdependencies — as they actually exist, not as they were last documented. No static CMDBs. No stale diagrams. The estate is modeled continuously, so the twin never drifts from reality.
View More
Continuously model your IT ecosystem – automatically, in real time.
QyrusAI builds and maintains a high-fidelity digital twin of your hybrid IT estate — servers, VMs, applications, and their interdependencies — as they actually exist, not as they were last documented. No static CMDBs. No stale diagrams. The estate is modeled continuously, so the twin never drifts from reality.
View More
Test changes and failures in a safe environment – before they’re real.
Every deployment, configuration change, or failure scenario can be run against the twin instead of production. Chaos engineering and resilience experiments happen in a “safe-to-fail” space, so teams can stress-test system stability without risking a single live transaction.
View More
Test changes and failures in a safe environment – before they’re real.
Every deployment, configuration change, or failure scenario can be run against the twin instead of production. Chaos engineering and resilience experiments happen in a “safe-to-fail” space, so teams can stress-test system stability without risking a single live transaction.
View More
See how a change will ripple through the system before you make it.
The twin’s dependency model turns “what if we deploy this?” into a predictable answer. Impact prediction surfaces downstream conflicts and risks during Change Management, so validated changes replace guesswork.
View More
See how a change will ripple through the system before you make it.
The twin’s dependency model turns “what if we deploy this?” into a predictable answer. Impact prediction surfaces downstream conflicts and risks during Change Management, so validated changes replace guesswork.
View More
Feed every autonomous workflow with a live, trusted picture of the estate.
The Intelligent Twin is the perception layer behind Auto Discover, Detect, Diagnose, and Resolve — giving every agentic workflow across the platform the current, contextual state it needs to act correctly, not just quickly.
View More
Feed every autonomous workflow with a live, trusted picture of the estate.
The Intelligent Twin is the perception layer behind Auto Discover, Detect, Diagnose, and Resolve — giving every agentic workflow across the platform the current, contextual state it needs to act correctly, not just quickly.
View More
Core Features
Built to Turn IT Complexity Into
a Predictable, Simulated Advantage.
Stop relying on stale documentation and live-fire testing. Discover how continuous modeling, safe-to-fail simulation, and impact prediction turn IT operations into a system you can trust before you act.
Continuous Ecosystem Modeling
Automatically discovers and models IT assets, configurations, and dependencies across hybrid environments in real time — no manual mapping required.
Safe-to-Fail Simulation
Runs chaos engineering and resilience experiments against the twin instead of production, enabling significantly more stress-testing with zero added risk
Autonomous Workflow Foundation
Provides the live perception layer that powers Auto Discover, Detect, Diagnose, and Resolve – turning modeling into action across the platform.
Change Impact Prediction
Simulates deployments and configuration changes before execution, identifying conflicts and downstream effects ahead of time.
A GLIMPSE ON THE NUMBERS
Outcomes & Benefits
Rationalize Faster. Reduce Risk. Fund the Transformation.
Secure a resilient, future-proofed IT ecosystem capable of adapting instantly. Achieve rigorous performance benchmarks while drastically lowering operational overhead.
20-
20
%
of Redundant legacy applications decommissioned
15 –
%
cost savings
25-
25
%
Reduction in migration timelines
20-
21
%
of IT resources reallocated
Continuous testing. Continuous learning. Continuous value.
Join leading enterprises
driving the loop forward.
Integration
Works With Your
Existing Enterprise Stack.
QyrusAI Modernize connects to your existing tools — CMDBs, ITSM platforms, cloud providers, and enterprise architecture repositories — so discovery enriches the tools you already use rather than replacing them.
CUSTOMER TESTIMONIALS
Enterprises running the loop
in their own words.
Real Teams. Real Outcomes. Real Confidence in what ships next.
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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.
Shawbrook Bank
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%.
Subaru
… 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.
Monument
Resources
Go Deeper on QyrusAI Intelligent Twin
Dive into our latest research, customer success stories, and practical implementation guides. See exactly how a continuously modeled, safe-to-fail digital twin turns IT complexity into a transparent, predictable operating advantage.
Blog
August 19, 2026 | 
18 min
User Acceptance Testing Best Practices: A Complete UAT Strategy Guide for SAP Teams
August 6, 2026 | 14 min
SAP UAT Test Cases: Templates, Examples & Design Best Practices
July 27, 2026 | 10 min
SAP IBP Testing: A Practical Guide for QA and Planning Teams
Read More
Load More 
Case Study
September 17, 2025 | 5 min
From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation
Read More
Food and Beverages
June 27, 2025 | 5 min
AI-Powered Testing Transforms One of the Largest Beverage Companies
Read More
BFSI
June 20, 2025 | 8 min
150% Efficiency Boost for Banking Client Using Device Farm
July 13, 2026 | 1 min
Meet Qyrus at the BFSI Innovation & Technology Summit India 2026
April 6, 2026 | 3 min
Qyrus at QonfX Bangalore: AI Testing, Context Engineering & QA Innovation
March 23, 2026 | 3 min
STAREAST 2026: Joining the Quality Engineering Conversation in Orlando
August 13, 2026 | 2 min
Qyrus Named in Gartner’s Market Overview for API and MCP Testing Tools
August 12, 2026 | 2 min
Modernize SAP Before 2027 Without Letting Defects Reach Go-Live
June 29, 2026 | 3 min
Why UK Fintechs Are Making QA Central to Operational Resilience.
August 13, 2026 | 2 min
Qyrus Named in Gartner’s Market Overview for API and MCP Testing Tools
August 12, 2026 | 2 min
Modernize SAP Before 2027 Without Letting Defects Reach Go-Live
June 29, 2026 | 3 min
Why UK Fintechs Are Making QA Central to Operational Resilience.
Blog
August 19, 2026 | 18 min
User Acceptance Testing Best Practices: A Complete UAT Strategy Guide for SAP Teams
August 6, 2026 | 14 min
SAP UAT Test Cases: Templates, Examples & Design Best Practices
July 27, 2026 | 10 min
SAP IBP Testing: A Practical Guide for QA and Planning Teams
Case Study
Case Study
September 17, 2025 | 5 min
From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation
Read More
Food and Beverages
June 27, 2025 | 5 min
AI-Powered Testing Transforms One of the Largest Beverage Companies
Read More
BFSI
June 20, 2025 | 8 min
150% Efficiency Boost for Banking Client Using Device Farm
Events
July 13, 2026 | 1 min
Meet Qyrus at the BFSI Innovation & Technology Summit India 2026
April 6, 2026 | 3 min
Qyrus at QonfX Bangalore: AI Testing, Context Engineering & QA Innovation
March 23, 2026 | 3 min
STAREAST 2026: Joining the Quality Engineering Conversation in Orlando
Reports
August 13, 2026 | 2 min
Qyrus Named in Gartner’s Market Overview for API and MCP Testing Tools
August 12, 2026 | 2 min
Modernize SAP Before 2027 Without Letting Defects Reach Go-Live
June 29, 2026 | 3 min
Why UK Fintechs Are Making QA Central to Operational Resilience.
Whitepaper
August 13, 2026 | 2 min
Qyrus Named in Gartner’s Market Overview for API and MCP Testing Tools
August 12, 2026 | 2 min
Modernize SAP Before 2027 Without Letting Defects Reach Go-Live
June 29, 2026 | 3 min
Why UK Fintechs Are Making QA Central to Operational Resilience.
FREQUENTLY ASKED QUESTIONS
Common Questions About QyrusAI Modernization
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incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud.
How is this different from a CMDB or an EA repository like LeanIX?
A CMDB is maintained by FTEs and is outdated within days of any infrastructure change. LeanIX and similar repositories are populated by surveys — they capture what people say is true, not what the system is actually doing. QyrusAI discovers your estate by observing it in real time: reading APIs, data flows, system calls, and infrastructure events. The graph stays current because it is updated continuously, not because someone remembers to update a ticket. The moment your CMDB says a dependency does not exist, QyrusAI’s observed graph is the source of truth.
Does Qyrus Modernize work with SAP S/4HANA migrations?
Yes — and SAP migrations are one of the highest-urgency use cases. QyrusAI reads your SAP environment in read-only mode, inventories all RFCs, IDocs, OData services, SOAP connections, CPI integrations, background jobs, and Z-objects, and produces a blast-radius score for every proposed S/4HANA change. The Estate X-Ray and Phase Zero governance workflow are specifically designed for ECC-to-S/4HANA programmes with fixed cutover dates.
How long does the initial discovery scan take?
The first scan of a mid-market estate (200–400 applications) typically produces an initial topology map within 48–72 hours. The map enriches continuously from that point — adding confidence detail, business-service attribution, and dependency validation as more signals are observed. A Phase Zero engagement (discovery, dependency mapping, and modernization prioritization) is typically scoped at 4–6 weeks.
Does it require agent installation on every system?
No. QyrusAI reads from APIs, connectors, log streams, and cloud provider metadata — it does not require an agent on every application or server. For some legacy on-premise environments a lightweight collector may be deployed, but this is scoped per engagement and never required across the full estate before value is delivered.
What happens to the modernization plan if the estate changes while the programme is running?
The Knowledge Graph is continuously updated. If a new application is deployed, a dependency changes, or a system is decommissioned mid-programme, the change is reflected in the graph and the modernization wave plan is re-scored automatically. Your transformation plan is always based on the current estate, not the estate as it was when the programme started.
Does Qyrus Modernize integrate with Assure for testing?
Yes — this is the core joint value of the two pillars. Every modernization slice Modernize produces is automatically handed to Assure for equivalence testing. Assure generates test cases from the code analysis output, runs them against the modernized slice, and produces an evidence record before any release decision is made. The conversion is never marked complete until Assure has proved it works.
A CMDB is maintained by FTEs and is outdated within days of any infrastructure change. LeanIX and similar repositories are populated by surveys — they capture what people say is true, not what the system is actually doing. QyrusAI discovers your estate by observing it in real time: reading APIs, data flows, system calls, and infrastructure events. The graph stays current because it is updated continuously, not because someone remembers to update a ticket. The moment your CMDB says a dependency does not exist, QyrusAI’s observed graph is the source of truth.
Yes — and SAP migrations are one of the highest-urgency use cases. QyrusAI reads your SAP environment in read-only mode, inventories all RFCs, IDocs, OData services, SOAP connections, CPI integrations, background jobs, and Z-objects, and produces a blast-radius score for every proposed S/4HANA change. The Estate X-Ray and Phase Zero governance workflow are specifically designed for ECC-to-S/4HANA programmes with fixed cutover dates.
The first scan of a mid-market estate (200–400 applications) typically produces an initial topology map within 48–72 hours. The map enriches continuously from that point — adding confidence detail, business-service attribution, and dependency validation as more signals are observed. A Phase Zero engagement (discovery, dependency mapping, and modernization prioritization) is typically scoped at 4–6 weeks.
No. QyrusAI reads from APIs, connectors, log streams, and cloud provider metadata — it does not require an agent on every application or server. For some legacy on-premise environments a lightweight collector may be deployed, but this is scoped per engagement and never required across the full estate before value is delivered.
The Knowledge Graph is continuously updated. If a new application is deployed, a dependency changes, or a system is decommissioned mid-programme, the change is reflected in the graph and the modernization wave plan is re-scored automatically. Your transformation plan is always based on the current estate, not the estate as it was when the programme started.
Yes — this is the core joint value of the two pillars. Every modernization slice Modernize produces is automatically handed to Assure for equivalence testing. Assure generates test cases from the code analysis output, runs them against the modernized slice, and produces an evidence record before any release decision is made. The conversion is never marked complete until Assure has proved it works.
Move From Static Lists to
Living Intelligence.
Arrange an exclusive thirty-minute strategy session with an infrastructure architect—we will expose your hidden vulnerabilities and demonstrate precisely how automated mapping drastically curtails resolution windows across your enterprise.
Talk to an expert