Ulpi provides a control plane that connects and synchronizes multiple AI coding assistants—such as Claude Code, GitHub Copilot, Cursor, and Windsurf—so they can operate in parallel without causing merge conflicts or overlapping edits. The desktop and CLI tool orchestrates agents through a unified API and a Slack‑like coordination layer, reducing token usage by up to 49% and accelerating pull‑request approvals. It offers end‑to‑end encryption, sub‑50 ms response times, and a marketplace for reusable workflow templates, targeting software engineering and DevOps teams that rely on multiple AI providers.
Funding
Funding not disclosed
Founders
Product
Problem
Development teams using multiple AI coding assistants often encounter merge conflicts, overlapping edits, and lack of coordination between agents, leading to slower pull‑request cycles and higher token usage.
Solution
ULPI offers a control plane that connects and synchronizes various AI providers—such as Claude Code, GitHub Copilot, Cursor, and Windsurf—allowing them to operate in parallel without stepping on each other’s changes. The platform runs as a desktop application and CLI tool, providing sub‑50 ms response times and end‑to‑end encryption to keep code private. By orchestrating agents through a unified API and a “Slack‑for‑AI” coordination layer, ULPI reduces token consumption, eliminates merge conflicts, and accelerates PR approvals. Developers can install ULPI MCP servers in minutes, integrate existing workflows, and leverage a marketplace of reusable workflow templates and memory systems that let agents retain relevant context while forgetting noise.
Target Audience
Primary customers are software engineering teams and DevOps groups that rely on multiple AI coding assistants and need reliable, production‑ready coordination of AI agents.
Features
- Unified connector for unlimited AI providers and parallel sub‑agents from a single control plane
- Real‑time multi‑agent orchestration that prevents overlapping edits and merge conflicts
- Salience‑based memory system enabling agents to retain important context and reduce token usage by up to 49%
- Sub‑50 ms response latency to support production‑grade AI agent deployments
- End‑to‑end encryption and SOC 2‑type security guarantees that code is never used to train external models
- Public APIs and marketplace for community‑driven workflow templates and skills reuse
- Desktop and CLI interfaces for seamless integration into existing development toolchains