Ensure Trusted Data and AI Readiness

HEAD OF AI, DATA PLATFORMS & GOVERNANCE

AI Is Only As Good As the Data Beneath Itand the Governance Around It.

Enterprise AI programmes are stalling — not because the models are wrong, but because the data pipelines feeding them are unvalidated, the agents operating on them have no enterprise context, and the governance layer needed to put them into production does not yet exist.

Untrusted pipelines.

AI pilots with no enterprise context.

Validated pipelines.

Agents grounded in the enterprise graph. Every AI action evidenced and reversible.

CHALLENGES

AI Pilots Are Succeeding. Production Is Where They Go to Die.

AI pilots succeed in the lab, but production exposes every upstream data flaw and governance gap. Discover how to eliminate hallucinations, ground agents in enterprise context, and guarantee audit-ready compliance before your next rollout.

Untrusted data pipelines undermine AI outcomes

80%+ of AI project failures trace to data quality issues upstream of the model; the model is blamed for the pipeline's problem

AI initiatives stall because the models have no enterprise context

LLMs and agents hallucinate or produce irrelevant outputs when they lack grounding in the specific structure of the enterprise.

Agentic AI in production is hard to govern and harder to prove safe

agents taking automated actions in production with no bounded policy, no audit trail, and no rollback capability are a board-level risk event waiting to happen

Models and agents degrade silently after deployment

data drift, distribution shift, and upstream pipeline changes silently degrade model performance; the business usually notices before monitoring does

Regulatory scrutiny is arriving before the governance platform is ready

EU AI Act, NIST AI RMF, DPDP Act, and sector-specific AI guidance are creating compliance obligations that most AI teams are not equipped to evidence

AI supply-chain risk

Consolidating tools without knowing what connects to what creates the risk of removing something that something else depends on

THE RESOLUTION

AI is only as good as the context and the data beneath it validate every pipeline, ground every agent in a living enterprise knowledge graph, and govern what you put into production.

LOREM IPSUM DOLORSIT. AMETARS.

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The transformation table

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Before

Legacy Code

Various coding standards and styles
No documentation
Brittle test suites built on Selenium
Manual regression cycles taking weeks
Risk of breaking unknown dependencies
Knowledge locked in individuals, not systems

AFTER

Regression Code Base

Codeless business process tests
Automated test generation and execution
Self-healing test scripts (Healer AI)
Continuous regression coverage
Full dependency visibility before any change
AI-maintained, not person-dependent

LOREM IPSUM DOLORSIT. AMETARS.

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The Joint Motion Diagram

How Modernize + Assure Work in One Motion. Lorem Ipsum Dolor Sit Amet.
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IT / Enterprise Architect
Analyze legacy code architecture automatically
QALead
Generate a regression suite from the analyzed code base
Dev Team
Convert Selenium scripts to codeless tests at scale
Compliance / Audit
Get a test evidence trail for every conversion
CIO
Track modernization progress against a real-time roadmap

Qyrus Ai Engine

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Modernize

Code Analysis

Model all Java classes, libraries, architecture

Modernize

Code Analysis

Model all Java classes, libraries, architecture

Modernize

Code Analysis

Model all Java classes, libraries, architecture

Modernize

Code Analysis

Model all Java classes, libraries, architecture

THE UNLOCK: QyrusAI’s code analysis models the legacy test architecture; converts it to codeless. Conversion path validated.

CORE CAPABILITIES

uilt for AI and Data Leaders Who Must Deliver Trusted AI Not Just Fast AI.

Ensure every model output is accurate, grounded, and fully governed. Discover how pipeline validation, Enterprise Knowledge Graph context, and SEER guardrails turn risky AI experiments into trusted, audit-ready enterprise systems.

Data Testing & Pipeline Validation

Continuous ETL/ELT/ML pipeline testing. Business-logic assertion layer on top of infrastructure validation. DataChain test data management for controlled AI training and evaluation datasets. Anomaly detection in the data layer before it reaches the model.

LLM, RAG & Agentic AI Validation

Enterprise Knowledge Graph - AI Context Layer

SEER Guardrailed Agentic Framework

AI Governance & Regulatory Evidence

Model & Agent Monitoring

Enterprise Knowledge Graph - AI Context Layer

LOREM IPSUM DOLORSIT. AMETARS.

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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.