AI lets you move fast but it's breaking things quietly.
Conductor prevents KPIs from tanking, by auto refactoring human and AI generated code.
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41% of new code is written by AI which is breaking your codebase and impacting your KPIs. Conductor is the only product trained to identify and fix code problems that directly impact your KPIs - starting with web performance and web accessibility.
As your engineers write new code, Conductor will review, identify, and automatically fix issues that impact your KPIs. It's like working with the smartest web engineer you know but available for everyone.
Every review is ran through a proprietary confidence scoring system to ensure only the most valuable feedback is shared
Conductor limits the noise to ensure it only provides feedback that is relevant and actionable, not broad and generic.
Configurable to the metrics and standards your organization cares about.
Conductor has context of your codebase to provide feedback that... fits in.
Your code and requirements stay completely within your own stack. None of your code or requirements are sent back to our servers or stored in our system.
AI agent decisions are stored on your servers for visibility and traceability.
Conductor integrates effortlessly with your organization's LLM setup, leveraging your existing access provisions.
Whether you're hosting models through AWS Bedrock, Azure AI, directly with providers like Anthropic or OpenAI, or running open-source models like Meta's Llama on your own hardware, Conductor adapts to your needs.
Our secure design ensures Conductor operates within your environment, connecting to your tools—including LLMs—the way you choose, making it one of the best ai tools for software testing.
Conductor is currently focused and specialized for JavaScript, and TypeScript codebases. This specialization allows Conductor to write high quality passing tests for complex aspects of JavaScript and Typescript codebases.
Conductor sets itself apart as one of the best AI tools for software testing by leveraging specialized multi-agent agentic ai workflows that mimic human collaboration to create, review, and refine code. This approach delivers higher-quality tests uniquely tailored to an organization's coding standards. Unlike generic AI code generation tools like GitHub Copilot or Cursor, Conductor features automated self-healing loops, where agents independently run and iterate on tests, continuously adapting to the codebase's nuances without user intervention.
Additionally, Conductor enables teams to define specific testing guidelines and integrates with backlogs to incorporate business logic, ensuring tests align with both code correctness and underlying business requirements. This deeply customized and iterative process goes far beyond the capabilities of generic code generation tools.
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Conductor, one of the best AI tools for software testing, ensures its tests are genuinely valuable by tailoring them to the specific context of your codebase and business requirements—be they user stories, epics, PRDs, RFCs, or any combination thereof—while also aligning with your organization’s unique coding standards and practices. Unlike generic code generation tools, Conductor automatically adapts to each team’s development structure, guidelines, and preferences, and can be further refined by technical experts (like Principal Engineers) to support new practices. Through customizable multi-agent workflows, testing guidelines, self-healing loops, and backlog integrations, Conductor delivers context-aware, company-specific testing that goes beyond mere coverage metrics to ensure meaningful quality, security, and value at scale.
Conductor gathers business context in several ways. It can draw from specific business documents or files containing key information—such as PRDs, product visions, or OKRs—and it also integrates with popular backlog tools like Atlassian Jira and Microsoft Azure DevOps. This allows Conductor to automatically pull in relevant context, ensuring comprehensive test coverage that make sense for your business.
Currently, Conductor’s agentic AI multi-agent workflows employ five agents as part of our standard, out-of-the-box setup. Each agent focuses on a distinct aspect of the workflow—such as writing tests, reviewing tests, or running tests—ensuring comprehensive coverage. However, the agentic AI architecture is designed to be flexible: we can introduce additional specialized agents for customers with complex or highly regulated environments. This adaptability allows us to fine-tune the agentic AI system to match unique needs while maintaining a strong focus on quality and precision.
