Dependency Mapping | Auto Discovery | Knowledge Graph
Eliminate Blind Spots. Command Your Architecture.
Modern development stacks render manual asset tracking completely obsolete. Our platform continuously observes your environment, constructing a living digital twin to guarantee high availability, accelerate migrations, and establish proactive operational control.
Challenges
The Inherent Risks of
Static Infrastructure Documentation.
Relying on manual updates in ephemeral environments guarantees that critical knowledge is always out of date. Operating without structural visibility severely degrades incident response times and modernization efforts.
Obsolete Tribal Knowledge
Relying exclusively on subject matter experts to understand intricate configurations isolates crucial intelligence, causing severe bottlenecks during high-stakes outages and architectural migrations.
Blind Modernization Risks
Migrating applications without understanding hidden interconnections frequently triggers unforeseen cascading failures, severely jeopardizing overall system resilience and extending project timelines.
Reactive Triage Delays
Without a live structural map, help desks route tickets based on historical guesswork, forcing engineers to spend critical hours attempting manual root cause analysis.
Ephemeral Resource Chaos
The sheer volume of modern cloud-native components renders human-led tracking impossible, leaving massive gaps in operational awareness as soon as environments scale dynamically.
How it works
Identify Assets. Correlate Patterns.
Visualize Dependencies. Guide Operations.
Explore the four-step workflow of dependency mapping: continuously discovering infrastructural assets, correlating operational patterns, modelling active interdependencies into a digital twin, and guiding autonomous agentic actions.
Servers, VMs, and network configs mapped
QyrusAI’s Auto-Discovery Engine autonomously identifies every server, virtual machine, and network configuration across the environment, bypassing manual tracking entirely. This continuous scan captures infrastructure changes the instant they occur, rather than at scheduled audit intervals. The result is a structural blueprint that stays accurate even as the underlying architecture shifts hour to hour.
Legacy and modern stacks unified
Alongside infrastructure, QyrusAI maps the full application landscape spanning both legacy systems and modern, cloud-native stacks. Each application is captured with its configurations, so the platform understands not just that a system exists, but how it’s built. This unified view is what lets teams see legacy and modern components side by side, instead of managing them through separate tools.
Dynamic resources captured before they vanish
Modern environments spin up and tear down containers, serverless functions, and short-lived instances faster than any manual process can record. QyrusAI’s discovery captures these ephemeral components as they appear, not after the fact. This keeps the dependency picture complete even in highly dynamic, auto-scaling environments where resources exist for minutes rather than months.
System model updates as environments change
A discovery process that runs once quickly becomes outdated in an environment that never stops changing. QyrusAI keeps the system topology synchronized in the background, reflecting new deployments, decommissioned assets, and shifted configurations automatically. This gives every downstream capability — correlation, modeling, and guided action — a foundation that’s never stale.
Incidents, tickets, and requests pulled together
QyrusAI ingests incident, service, and problem tickets directly from ITSM systems rather than treating them as a separate reporting layer. These signals are correlated against the discovered infrastructure and application landscape in real time. This connects what’s being reported to what actually exists in the environment, instead of leaving the two disconnected.
Recurring issues surfaced before they escalate
Repeated service requests and recurring ticket patterns are rarely random — they usually point to an underlying dependency issue. QyrusAI identifies these trends and flags statistical anomalies across the ticket history automatically. Surfacing the pattern early means teams can address the root dependency instead of repeatedly closing the same symptom.
Learning engine routes tickets accurately
QyrusAI’s correlation layer is trained specifically for Assignment Group detection, as well as ticket intent and priority classification. Instead of relying on keyword matching, it learns what a ticket actually means in the context of the dependency map. This reduces misrouted tickets and gets issues in front of the right team faster.
Reduce dependence on individual experts
Critical context about how systems connect has traditionally lived in the heads of a handful of subject matter experts. QyrusAI’s correlation engine captures this tribal knowledge as it processes tickets, incidents, and requests, turning isolated expertise into shared, queryable intelligence. This means teams no longer wait on one person’s availability to understand why a system behaves the way it does.
Discovered and correlated data unified
Every discovered asset and correlated signal feeds directly into QyrusAI’s Knowledge Graph, the structural core of the platform. Rather than a static database, the graph functions as a learning engine that continuously refines its understanding of how components relate. This transforms raw, disconnected data points into context-rich intelligence that every other capability draws on.
A living digital twin of IT
The Knowledge Graph is rendered into a Digital Twin — a dynamic, living representation of the entire IT ecosystem. Unlike a diagram drawn at one point in time, this model updates as the underlying environment changes. It gives architects a single, current reference for how every system actually behaves, not how it was originally designed.
See how old and new interact
Legacy systems and modern cloud-native services rarely interact cleanly, and that friction is exactly where outages tend to originate. QyrusAI’s model visualizes precisely how these two worlds connect, dependency by dependency. This clarity is what lets teams evaluate a change with confidence before it’s executed, rather than discovering the impact afterward.
Highlight what needs attention first
Within the modeled ecosystem, QyrusAI highlights assets that are critical to business operations, generating excessive noise, or showing signs of fragility. This scoring feeds directly into assessment and rationalization work elsewhere on the platform, giving those decisions a dependency-aware foundation. Flagging risk at the modeling stage means prioritization starts from evidence, not guesswork.
Agents perceive, plan, and act
The modeled dependency intelligence feeds autonomous agents that perceive context, plan next steps, and act on gathered structural knowledge. This moves the platform beyond rigid, rule-based automation toward context-aware decision-making. The system understands not just that a component is down, but exactly which business services depend on it.
From assessment through migration to retirement
Dependency intelligence guides decisions at every stage of an application’s lifecycle, not just at launch. During assessment, it identifies critical, noisy, or fragile assets; during migration, it validates that hidden interconnections won’t break; during retirement, it confirms an application can be safely removed. This continuity means guidance doesn’t reset with every new phase of work.
Confirm impact before executing any change
Before a change is executed anywhere in the environment, QyrusAI checks it against the dependency model to predict what it will affect. This validation step is what makes automated migration testing and decommissioning impact analysis possible elsewhere on the platform. Teams get a clear read on consequences before committing to an action, not after.
Configurations evolve as objectives change
Guided action doesn’t end once a workflow executes — the outcome feeds back into the Knowledge Graph and refines future recommendations. This feedback loop lets configurations continuously adapt rather than drift out of alignment with enterprise objectives. Over time, the system doesn’t just respond to change; it learns from it.
Core Features
Engineered for Comprehensive
Architectural Visibility and Automated Precision.
Replace static documentation with a continuously evolving intelligence engine. Empower your engineering teams to manage complex lifecycles with absolute structural certainty.
Autonomous Asset Discovery
Bypasses manual entry entirely by continuously detecting virtual machines, application landscapes, and network configurations across modern and legacy developmental stacks.
Dynamic Knowledge Graph
Functions as an active learning engine that visualizes interdependencies instantly, transforming raw infrastructural data points into context-rich, actionable operational insights.
Intelligent Triage Routing
Analyzes ticket intent and priority autonomously to route critical issues perfectly, bypassing traditional help desk bottlenecks and historical guesswork completely.
Lifecycle Impact Analysis
Provides deep structural validation prior to decommissioning software or migrating workloads, identifying hidden connections to guarantee zero disruption during modernization.
A GLIMPSE ON THE NUMBERS
Accelerate Migrations. Maximize Uptime. Optimize Resource Allocation.
Secure a resilient, future-proofed IT ecosystem capable of adapting instantly. Achieve rigorous performance benchmarks while drastically lowering operational overhead.
IT estate visible on first discovery
Run cost reduction — capacity for change
Lower migration volume with Phase Zero
Faster modernization programme execution
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.
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 advanced methodologies for mapping dynamic environments. Provide your teams with the ultimate blueprint for seamless technological evolution.
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 Modernization
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How is this different from a CMDB or an EA repository like LeanIX?
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.
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.
