Rubbr
Rubbr is an AI-driven software delivery platform that understands existing applications directly from their code, enabling business, product, and engineering teams to collaborate on turning requests into real software changes. The platform uses a governed multi-agent pipeline—from intent capture through PRD generation, code review, and pull request creation—while remaining LLM-agnostic and deployable within a customer's own infrastructure. Engineering teams set the rules for who can contribute and what requires approval.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
For existing applications, every change—even a small one—requires reconstructing system context, assessing impact, translating business intent into a buildable specification, and finding engineering capacity. In most established organizations, this context does not exist in a clean, structured form; it lives in fragmented legacy code, incomplete documentation, undocumented trade-offs, and the heads of a few key individuals. This creates bottlenecks, dependencies on key people, and delivery delays that can exceed the implementation time itself.
Solution
Rubbr is a controlled AI-driven software delivery platform that continuously understands what an application does, how each feature is implemented, and the rules and dependencies that govern it. The platform introduces a persistent, versioned Context File System built during setup—including system maps, domain boundaries, interface contracts, invariants, ADR history, coding standards, and risk constraints—so agents operate within a defined model of the system rather than trying to figure it out. Every request is analyzed, scoped, and specified against the real application before anything is built, and each feature flows through a governed pipeline: intent capture, PRD generation, architectural decision record, delivery plan, code generation, code review, test generation, and pull request creation. Rubbr is LLM-agnostic by design, allowing organizations to choose models based on cost, latency, data residency, or capability without rearchitecting. The platform also orchestrates existing AI agents within a customer's stack, structuring and constraining them rather than replacing them.
Target Audience
Primary customers are product owners and software organizations running business-critical or legacy systems that cannot afford uncontrolled change, particularly those with data residency requirements, IP sensitivity, or token-economics constraints.
Features
- Context File System that makes business rules, architectural constraints, ownership boundaries, and historical decisions explicit, versioned, and auditable
- Governed multi-agent pipeline covering intent capture, PRD generation, architectural decision records, delivery plans, code generation, code review, test generation, and pull request creation
- LLM-agnostic architecture that runs on models including Opus, Codex, and Mistral's Devstral 2, with the canonical context layer independent of any specific provider
- Specialized agents at each pipeline stage that receive structured context, reducing variance and increasing traceability across the delivery chain
- Sovereign deployment where canonical context never leaves the customer's infrastructure, supporting data residency, IP sensitivity, and regulatory requirements
- Orchestration of existing AI agents within a customer's stack, structuring and constraining them rather than replacing them
- Engineering-defined rules that determine who can contribute to software changes, what requires approval, and what Rubbr can implement