Nozomio

Nozomio Labs is an Applied AI Research Lab focused on empowering software developers to manage AI agents at scale while preserving code quality and context integrity. The platform targets the end-to-end software lifecycle—from development and QA to security audits and deployment—by providing systems to orchestrate AI agents with strong context management, built-in QA, and validation.

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Overview

• Mission: Transform how software is built by enabling small teams to harness AI agents for large-scale software operations without sacrificing codebase understanding.
• Vision: AI-assisted software development where agents understand and operate within your codebase safely and effectively.
• Current focus: Context management and codebase understanding; beta products include nia | ai debugging (next!).

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How It Works

1. AI agents are configured to operate over your software lifecycle, with explicit context and ownership of code regions.
2. The platform uses context managers and validation pipelines to ensure agents reason about the correct code and state, reducing the risk of context drift.
3. Built-in QA and validation systems continuously test agent actions against the codebase, providing feedback before deployment.
4. Orchestration enables small teams to coordinate multiple agents across tasks such as development, QA, security audits, and deployment.

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Current Beta Products

• nia | ai debugging (next!)
• AI agent orchestration tooling for large-scale software operations

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Why Nozomio

• Empowers developers to leverage intelligent AI systems without sacrificing code quality.
• Focuses on context integrity across the entire software lifecycle.
• Aims to simplify managing multiple AI agents in parallel for complex projects.

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How to Get Involved

• If you build software at scale and want to harness AI agents with strong context management, contact the team.
• Founding AI Engineer role available: experience with LLMs, Retrieval-Augmented Generation (RAG) systems, and AI orchestration preferred.
• Apply: [email protected] (Arlan)

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Core Features

• Context-aware AI agent orchestration across the software lifecycle
• Built-in QA and validation systems to ensure code safety and quality
• Real-time context management to minimize codebase understanding gaps
• Support for development, QA, security audits, and deployment workflows
• Beta tools for AI debugging (nia) and upcoming enhancements
• Collaboration-friendly environment for small teams to coordinate multiple agents
• Emphasis on enabling developers rather than replacing them