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Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
Open-source AI search and vector database platform for building large-scale search, RAG, and recommendation systems.
Free trial available
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
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- ✦Combined vector, text, and structured search
- ✦Distributed machine-learned ranking at query time
- ✦Streaming search mode for cost-efficient personal/private data
- ✦Support for retrieval-augmented generation pipelines
- ✦Continuous deployment and automated scaling
- ✦Open-source core with a managed cloud option
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
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- →Building large-scale enterprise search engines
- →Powering RAG pipelines that need strong retrieval relevance
- →Building recommendation and ad-targeting systems
- →Search over personal/private data at lower indexing cost