Qyrus Named a Leader in The Forrester WaveTM: Autonomous Testing Platforms, Q4 2025 – Read More

[MODERNIZE] + [ASSURE] + [OPERATE]

One Qyrus. Three AI businesses. One continuous system.

Agentic AI has made modernization, quality engineering, and application operations one continuous system — not three separate platforms that occasionally hand off to each other. QyrusAI is built on a shared Knowledge Graph, a shared orchestration layer (SEER), and a shared evidence store (Qhive)

BEFORE:

Three teams. Three tools. Three separate views of the same system.

AFTER:

One enterprise reality. Every action by one agent is context for the next.

Problem Stack

Three separate problems. Or one connected system failure

When modernization, testing, and operations function in silos, individual project successes often mask deep systemic vulnerabilities. True enterprise resilience requires tearing down these walls so every team and AI agent shares a single, unified reality.

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Transformation projects that succeed in isolation —

and fail at integration because nobody mapped what connected to what across the whole estate

Test suites that prove what was built —

but never know what production has already broken

Operations teams that remediate incidents —

but never send the knowledge back to quality or to modernization

An AI agent for testing, an AI agent for monitoring, an AI agent for modernization —

none of which share context, evidence, or memory

Governance that is bolted on as a compliance exercise —

not built into every action every agent takes

Release pipelines that gate on "all tests pass" —

without knowing whether those tests still reflect what production actually runs

Modernization roadmaps planned in a boardroom —

not updated by the signals the production estate emits daily

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THE RESOLUTION

QyrusAI makes modernization, quality, and operations one connected decision system — where every action enriches context, every decision cites evidence, and authority rises only when policy and approval allow.

One agentic platform that never stops learning.

Fragmented toolchains turn every handoff into a delay and every production incident into a multi-team investigation. By unifying transformation, quality, and operations into a single learning platform, context flows automatically across every stage of the lifecycle.

Before

Legacy Code

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AFTER

Regression Code Base

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Customer Journey Rail — In Practice

Modernizing mission-critical architecture requires absolute precision and zero tolerance for downtime.

  • CIO can: See transformation progress, test coverage, and production health in one real-time view

  • Enterprise Architect can:Map every dependency before any modernization slice touches production
  • QA Lead can: Prove every modernized slice against the same business processes the legacy system ran — with equivalence evidence
  • SRE / Ops Lead can: Monitor the modernized platform from day one with a pre-built failure model
  • Risk / Compliance can: Access a single audit record covering transformation, testing, deployment, and production events

Qyrus Ai Engine

A global bank is modernizing its core banking platform from a 25-year-old legacy system to a cloud-native architecture. 300 business processes. Regulatory scrutiny. Zero tolerance for production incidents during transition.

Modernize

UNDERSTAND

Estate X-Ray maps 300 business processes, all dependencies, blast-radius for every change

Modernize

CHANGE

One business process slice at a time; optimal conversion sequence; governance at every decision point

ASSURE

PROVE

SEER generates equivalence tests for the modernized slice; SAP-native testing where applicable; evidence stored in Qhive

ASSURE + OPERATE

DEPLOY

Evidence from Assure gates the production promotion; Operate health-checks all downstream dependencies

OPERATE

RUN

AIOps monitors the modernized process; self-healing handles known patterns; any production failure flows back to UNDERSTAND

THE UNLOCK: When all three pillars run on one intelligence layer, every stage of the lifecycle improves the next. The system learns continuously — not just from what was planned, but from what actually happened in production.

Contact us to learn more.

Joint Capabilities

One platform. Six layers.
Three purposes. Zero handoff gaps.

Enterprise agility isn’t achieved by chaining together disconnected tools; it requires a single, unified intelligence layer

Qyrus Modernize AI contributes:
Qyrus Modernize AI contributes:

Estate X-Ray — live, continuously-updated map of every system, dependency, and technical debt signal
Blast-radius scoring — before any change, the full cascade impact is known
Prioritization intelligence — operational cost + technical risk + business value → next modernization target
Phase Zero governance — transformation scoped, owned, and measured before it starts
Transformation record — every change logged in Qhive with full context, available to Assure and Operate

Qyrus Modernize AI contributes: Qyrus AI Quality Engineering contributes: Qyrus Application Run AI contributes: Shared Across All Three:

COMBINED FEATURES

Two disciplines. One outcome.

Neither pillar can do this alone. Modernize knows what the code is. Assure knows how to test it. Together, they convert legacy architecture into verified, continuously-tested codeless suites — without breaking anything along the way.

MODERNIZE

Continuous Application Discovery

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Dependency Mapping

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Code Analysis and Architecture Modeling

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Optimal Conversion Path Intelligence

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Technical Debt Scoring

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ASSURE

AI-Powered Test Generation

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Codeless Conversion of Selenium

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Self-Healing Test Scripts (Healer AI)

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SEER Orchestration Framework

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Test Evidence Trail for compliance

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A GLIMPSE ON THE NUMBERS

Enable Faster, More Reliable, and Cost-Efficient Retail Operations.

Maximize uptime, optimize infrastructure spend, and protect revenue during high-stakes shopping events. Explore how continuous quality and AI-driven operations deliver measurable performance gains across every omnichannel touchpoint.

0 %+

Reduction in customer-impacting incidents at peak

%+

Improvement in issue resolution time, omnichannel

0 %+

Optimization in SaaS/cloud spend across teams

2- x

Faster root cause identification, retail systems

Continuous testing. Continuous learning. Continuous value.

Join leading enterprises
driving the loop forward.

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.

Featured Image
Operate

October 16, 2026 | 6 min

The Blind Spot of Observability: Why IT Visibility Without Business Context Fails

Read More
Featured Image
Operate

October 15, 2026 | 4 min

Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It

Read More
Business Impact in IT Operations
Operate

October 14, 2026 | 5 min

The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value

Read More
Data-Analytics Leader Case Study
Assure

September 24, 2026 | 6 min

How a Global Data & Analytics Leader Scaled SAP GUI and Fiori Test Automation for Complex BRIM Workflows

Read More
coca-cola bottler case study
Case Study

September 17, 2025 | 5 min

From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation 

Read More
CCEP Case Study
Food and Beverages

June 27, 2025 | 5 min

AI-Powered Testing Transforms One of the Largest Beverage Companies

Read More
Featured Image

October 12, 2026 | 1 min

Next Stop, Madison: QyrusAI Joins the ASUG Community

Read More
Jack Henry Blog Featured Image

September 21, 2026 | 1 min

Meet QyrusAI at Jack Henry Connect 2026

Read More
BFSI Mumbai, 2026

July 13, 2026 | 1 min

Meet Qyrus at the BFSI Innovation & Technology Summit India 2026

Read More
Finops Featured Image1
Operate

October 13, 2026 | 2 min

FinOps: From Cost Visibility to Cost Predictability

Read More
Modernizing on JackHenry's Terms De-Risking the Symitar-to-SQL Transition Through Testing

September 28, 2026 | 2 min

The Jack Henry Platform Shift: De-Risking the Symitar-to-SQL Transition Through Testing

Read More
The AI Quality Gap Featured Image

September 17, 2026 | 3 min

AI Quality Engineering Solution for LLMs, RAG Pipelines, and MCP-Enabled Systems

Read More
Finops Featured Image1
Operate

October 13, 2026 | 2 min

FinOps: From Cost Visibility to Cost Predictability

Read More
Modernizing on JackHenry's Terms De-Risking the Symitar-to-SQL Transition Through Testing

September 28, 2026 | 2 min

The Jack Henry Platform Shift: De-Risking the Symitar-to-SQL Transition Through Testing

Read More
The AI Quality Gap Featured Image

September 17, 2026 | 3 min

AI Quality Engineering Solution for LLMs, RAG Pipelines, and MCP-Enabled Systems

Read More

Blog

Featured Image
Operate

October 16, 2026 | 6 min

The Blind Spot of Observability: Why IT Visibility Without Business Context Fails

Read More
Featured Image
Operate

October 15, 2026 | 4 min

Data-Rich, Context-Poor: Why Enterprise AI Fails Without Context—And How to Fix It

Read More
Business Impact in IT Operations
Operate

October 14, 2026 | 5 min

The Cost of Flying Blind: Why Disconnected IT Operations Bleed Business Value

Read More

Data-Analytics Leader Case Study
Assure

September 24, 2026 | 6 min

How a Global Data & Analytics Leader Scaled SAP GUI and Fiori Test Automation for Complex BRIM Workflows

Read More
coca-cola bottler case study
Case Study

September 17, 2025 | 5 min

From Bottlenecks to Breakthroughs: A Coca-Cola Bottler’s Quality Transformation 

Read More
CCEP Case Study
Food and Beverages

June 27, 2025 | 5 min

AI-Powered Testing Transforms One of the Largest Beverage Companies

Read More

Featured Image

October 12, 2026 | 1 min

Next Stop, Madison: QyrusAI Joins the ASUG Community

Read More
Jack Henry Blog Featured Image

September 21, 2026 | 1 min

Meet QyrusAI at Jack Henry Connect 2026

Read More
BFSI Mumbai, 2026

July 13, 2026 | 1 min

Meet Qyrus at the BFSI Innovation & Technology Summit India 2026

Read More

Finops Featured Image1
Operate

October 13, 2026 | 2 min

FinOps: From Cost Visibility to Cost Predictability

Read More
Modernizing on JackHenry's Terms De-Risking the Symitar-to-SQL Transition Through Testing

September 28, 2026 | 2 min

The Jack Henry Platform Shift: De-Risking the Symitar-to-SQL Transition Through Testing

Read More
The AI Quality Gap Featured Image

September 17, 2026 | 3 min

AI Quality Engineering Solution for LLMs, RAG Pipelines, and MCP-Enabled Systems

Read More

Finops Featured Image1
Operate

October 13, 2026 | 2 min

FinOps: From Cost Visibility to Cost Predictability

Read More
Modernizing on JackHenry's Terms De-Risking the Symitar-to-SQL Transition Through Testing

September 28, 2026 | 2 min

The Jack Henry Platform Shift: De-Risking the Symitar-to-SQL Transition Through Testing

Read More
The AI Quality Gap Featured Image

September 17, 2026 | 3 min

AI Quality Engineering Solution for LLMs, RAG Pipelines, and MCP-Enabled Systems

Read More

 FREQUENTLY ASKED QUESTIONS

Common questions about the Full Loop.

Whether you are planning a phased rollout or addressing CISO concerns about AI autonomy, we have the blueprints ready. Explore how our single intelligence layer replaces fragmented integrations with built-in security, shared evidence, and scalable governance.

Do we need to deploy all three pillars at once, or can we start with one?

Start with one. Every pillar delivers standalone value. The Full Loop activates as each additional pillar is added. Most customers start with Assure (fastest time to value, most immediate business case) or Modernize (transformation agenda with board sponsorship). Operate is typically added once the first pillar is delivering. The platform is designed for this: shared capabilities are available from day one; they serve more pillars as they are activated.

Yes — and this is specifically the architectural intent. The Knowledge Graph ingests from Estate X-Ray (Modernize), from SEER execution evidence (Assure), and from AIOps production signals (Operate). SAP, Salesforce, Workday, ServiceNow, and custom systems all have connectors. The graph holds the relationship and context model — what connects to what, what changed, what was tested, what broke. The richer the estate, the more valuable the graph.

Three meaningful differences: (1) Shared SEER orchestration — one orchestration contract across all three pillars; test, transformation, and run cards are all first-class SEER typed outputs composable without an integration project; (2) Shared Qhive evidence — a test outcome is available to Operate as deployment gate evidence and to Modernize as priority signal immediately, not after an API sync; (3) Shared Trust + Governance — one policy layer governs every agent across all three pillars; you do not manage three separate governance frameworks.

Three meaningful differences: (1) Shared SEER orchestration — one orchestration contract across all three pillars; test, transformation, and run cards are all first-class SEER typed outputs composable without an integration project; (2) Shared Qhive evidence — a test outcome is available to Operate as deployment gate evidence and to Modernize as priority signal immediately, not after an API sync; (3) Shared Trust + Governance — one policy layer governs every agent across all three pillars; you do not manage three separate governance frameworks.

One orchestration contract covering all three pillars, with subscription and policy determining which capabilities are visible and active. The platform is designed for one commercial relationship; distinct licensing per pillar is preserved for customers who need it, but the default is one agreement. Ask your solutions architect for the current commercial structure.

• STAY AHEAD, STAY IN THE LOOP.

One system that understands,
transforms, proves, runs, and learns — continuously.

45-minute walkthrough — we will map the full loop against your actual estate:
what you run, what you are transforming, and what you need to prove.