FOUNDED BY ORACLE QA VETERANS

Reliability for the
Agentic Era.

Specializing in *MCP Server Implementation*, *RAG Evaluation*, and *Agentic AI Testing*. We ensure your AI is safe, accurate, and enterprise-ready.

20 Years of Global
QA Pedigree

Moving from traditional enterprise testing at Oracle to the frontier of Generative AI. We understand that LLMs require a new paradigm of quality—moving from deterministic "Pass/Fail" to probabilistic "Evals."

20+
Years Experience
5+
AI Certifications

technical_stack.json

  • > TypeScript / Python
  • > Model Context Protocol (MCP)
  • > Ragas / DeepEval / Giskard
  • > Oracle DB / Vector Search
  • > Agentic Orchestration Testing

Our Heritage

QualiGenAI was founded by a veteran of the global software industry with a mission to bring deterministic reliability to AI.

"After 20 years in Quality Engineering—including a defining 9-year tenure at Oracle—I realized that the AI revolution lacked enterprise-grade verification."

The Journey

2015-2024: Oracle Era

Lead roles in Software Quality Engineering for enterprise database and cloud system reliability.

2005-2015: CSC/DXC Era

Mastering global delivery, Agile frameworks, and large-scale test automation.

Present: Founder at QualiGenAI

Implementing Agentic AI, MCP architectures, and RAG-LLM evaluation frameworks.

Our Expertise

High-fidelity Quality Engineering for the next generation of software, built on 20 years of enterprise standards.

Agentic AI Testing

We validate autonomous reasoning chains and tool-calling reliability.

  • • Chain-of-thought verification
  • • Tool-use accuracy audits

RAG Evaluation

Move beyond simple testing to probabilistic evaluation of retrieval pipelines.

  • • Context Relevancy & Faithfulness
  • • Vector DB latency optimization
Model Context Protocol

The Bridge to
Enterprise Data

Securely connect your proprietary ecosystem to LLMs using custom TypeScript MCP Servers.

Oracle-MCP Connector

Enable LLMs to query Oracle Databases using natural language safely. Built with the standard @modelcontextprotocol/sdk.

// MCP Tool Definition
{ "name": "fetch_db_schema",
"description": "Secure Oracle Query"
}

Moving from Pass/Fail
to Probabilistic Evals

Faithfulness

Ensures the answer is derived strictly from the retrieved context (No Hallucinations).

Relevancy

Measures how well the answer addresses the actual user query.

Robustness

Stress testing the AI against adversarial prompts and edge cases.

Our Evaluation Stack

Ragas DeepEval Giskard LangSmith Custom Python Benchmarks
FOUNDED BY ORACLE QA VETERANS

Reliability for the
Agentic Era.

Specializing in

  • MCP Server Implementation
  • RAG Evaluation
  • Agentic AI Testing

We ensure your AI is safe, accurate, and enterprise-ready.

PDF

2026 Capability Deck

Enterprise AI Quality Engineering & MCP Solutions

Oracle Veteran Founded • 20+ Yrs QA Excellence

Start Your AI Quality Audit

Ready to secure your LLM pipelines? Let's discuss your architecture.