We bring enterprise-grade verification to non-deterministic AI — specializing in MCP Server Implementation, RAG Evaluation & Agentic AI Testing, built on 20 years of global delivery.
High-fidelity quality engineering for the next generation of software, built on 20 years of enterprise standards.
We validate autonomous reasoning chains and tool-calling reliability in multi-agent systems before they reach production.
Move beyond simple Pass/Fail to probabilistic evaluation of retrieval pipelines with proven enterprise eval frameworks.
Securely connect your enterprise data ecosystem to LLMs using custom TypeScript MCP servers built to production standards.
Three enterprise-grade AI systems built, deployed and running in production — not demos, not prototypes.
Hybrid retrieval system (70% vector + 30% BM25) with 1536-dimension OpenAI embeddings, deployed on Render with Qdrant vector DB and PostgreSQL. Resolved 512 MB RAM constraint by migrating from sentence-transformers to OpenAI embedding API.
Multilingual WhatsApp chatbot (Telugu, Hindi, English) for temple pilgrims. Four-agent architecture with custom AWP orchestration protocol. 42-commit production audit sprint. Open source, MIT licensed.
AutoGen RoundRobinGroupChat with BugAnalyst agent (Jira/JQL queries) and AutomationAgent (Playwright browser control). MCP protocol workbenches, Docker containers, and Atlassian Jira Cloud integration.
Securely connect your proprietary Oracle databases, internal APIs, and enterprise ecosystems to LLMs using custom TypeScript MCP Servers — with no data leaving your control.
// MCP Tool Definition — Oracle Connector const oracleTool: Tool = { name: "query_oracle_db", description: "Execute natural language query on Oracle DB", inputSchema: { type: "object", properties: { query: { type: "string" }, schema: { type: "string" }, max_rows:{ type: "number" } } } }; // Register handler with MCP Server server.setRequestHandler( ListToolsRequestSchema, async () => ({ tools: [oracleTool] }) );
LLMs require a new quality paradigm — deterministic test suites can't capture hallucinations, context drift, or adversarial failure modes.
Every answer must be grounded strictly in retrieved context. We instrument your pipeline to flag any claim not traceable to a source document — eliminating hallucinations at the retrieval layer.
Measures how precisely a response addresses the user's actual intent — penalising incomplete answers, topic drift, and context over-retrieval that dilutes response quality.
Adversarial stress-testing against prompt injection, jailbreak attempts, and edge-case inputs. We find and patch failure modes in your AI system before real attackers — or users — do.
Our evaluation stack
Baseline scores are established per engagement during the initial audit phase.
Results vary by model, retrieval configuration, and dataset — we establish your specific thresholds, not industry averages.
" After 20 years in Quality Engineering — including a defining tenure at Oracle — I realized that the AI revolution lacked enterprise-grade verification. LLMs aren't deterministic systems. They require an entirely new discipline of quality engineering built on probabilistic evaluation, adversarial testing, and continuous monitoring. That's what QualiGenAI exists to provide.
Mastering global delivery, Agile frameworks, and large-scale test automation across enterprise accounts spanning multiple continents.
Lead roles in Software Quality Engineering for enterprise database and cloud system reliability — setting the standard for mission-critical delivery.
Implementing Agentic AI, MCP architectures, and RAG-LLM evaluation frameworks for enterprise clients — globally, from Hyderabad.
Get In Touch
Ready to secure your LLM pipelines? Let's discuss your architecture, identify risks, and build a roadmap to enterprise-grade AI reliability.
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