NiriZan

Continuous Evaluation Infrastructure for Production AI

"Inspection through Measurement" "Engineering Trust Through Continuous Evaluation"


CI Packaging Cross-platform

Why NiriZan?

Modern AI systems are probabilistic rather than deterministic. Traditional software testing alone cannot determine whether a retrieval pipeline, language model, or AI agent is performing correctly. NiriZan exists to provide continuous, reproducible evaluation infrastructure that enables teams to measure quality, detect regressions, compare experiments, and build confidence in production AI systems.

What is NiriZan?

NiriZan is an open-source continuous evaluation infrastructure for production AI systems. It enables engineers and researchers to systematically measure, benchmark, validate, and monitor the quality of:

  • Retrieval-Augmented Generation (RAG) pipelines
  • AI agents
  • Large Language Model (LLM) applications
  • Custom AI workflows

Unlike orchestration frameworks that focus on building AI applications, NiriZan focuses on engineering confidence in AI systems. It provides:

Capability Description
Reproducible evaluation pipelines Consistent, repeatable test runs across environments
Benchmark execution Standardized quality benchmarking for AI systems
Regression detection Automated flagging of quality drops between versions
Experiment tracking Full history of runs, configs, and results
Quality reporting Clear, actionable reports on system performance
Deployment-aware validation Checks tuned to pre-, during-, and post-deployment stages

Vision

The long-term vision of NiriZan is to become the engineering quality layer for production AI, ensuring that every AI application can be continuously measured before, during, and after deployment.

Where the Name Comes From

NiriZan is a fusion of two words from two languages, each contributing a core idea behind the project.

Niri Zan
Origin: নিরীক্ষা (Nirikkha) — Bangla/Bengali Origin: ميزان (Mīzān) — Arabic
Meaning: Inspection · Evaluation · Verification · Audit Meaning: Scale · Balance · Measurement · Criterion

Together, Niri + Zan captures the essence of the project: inspecting AI systems and measuring them against a balanced standard of quality.

License

Copyright (C) 2026 Redwan Rahman

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Author

Redwan Rahman github.com/Red1-Rahman

Key dates

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Credits

Author: Redwan Rahman

License

GNU General Public License v3 or later (GPLv3+) (GPL-3.0-or-later)

Requires

Python >=3.11

Provides Extra

dev

Tags

ai-observability ai-testing ci-cd-for-ai drift-detection evaluation-framework hallucination-detection llm-as-a-judge llm-benchmarking llm-evaluation llm-reliability opentelemetry-tracing rag-evaluation rag-triad regression-testing statistical-gating

Classifiers

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