Plan, code, build, deploy, and monitor — unified in one workspace. When everything shares one data model, AI finally has the context to do real work.
The average engineering team uses 10+ disconnected tools. Context is scattered, workflows break at the seams, and AI tools are useless — because they can only see one silo at a time.
When planning, code, CI, docs, and workflows share one data model, AI stops being a bolted-on feature and becomes an integral part of how you work.
Issues, branches, PRs, CI runs, docs, and workflows are all first-class objects in a single system. No sync plugins, no broken webhooks, no stale data.
Search your entire organization from a single bar — tasks, docs, repositories, code, PRs, CI logs, workflow runs. No more hunting across five different search UIs.
Your AI chat can reference a sprint, trace it to the PRs that implement it, read the CI output, and check the docs — all in one query. No copilot can do this across fragmented tools.
Because the AI sees everything, it can do everything: triage issues, write code, run tests, and open PRs — in fully isolated VMs, without leaving the platform.
AI assistants can only be as good as the context they have access to. When your data is spread across ten products, no amount of plugins can stitch it together.
The unified data model isn't a feature — it's the architecture that makes every AI feature possible.
Every action — whether taken by a developer or an AI agent — flows through the same RBAC, audit logs, and compliance controls. No shadow AI, no ungoverned automation.
One account gives you access to everything — projects, repos, CI, AI chat, agent compute. No separate credentials for separate tools. SSO for the entire platform.
The same roles and permissions govern developers and AI agents. An agent can only access what its owner can access — no privilege escalation, no backdoors.
Every commit, PR, deployment, and AI action is logged in one audit trail. Know exactly who or what changed your system, and when.
Replace five invoices from five vendors with one plan that covers planning, code hosting, CI/CD, AI, and agent compute. Costs go down, not up.
proxifai is a single command-line tool that talks to the entire platform. Plan issues, push code, trigger pipelines, query AI, and launch agents — all from your terminal.
One login, one API, one token. The same interface your AI agents use to automate everything — because if a human can do it from the CLI, an agent can do it too.
A beautiful UI means nothing if it goes down on deploy day. We build for the worst case first — then make it fast, then make it pretty.
Production clusters in EU and US with independent data planes. If one region has issues, the other keeps running. Your data stays in the region you choose.
Database replicas, self-healing nodes, and rolling deployments. When hardware fails — and it will — the platform recovers without you noticing.
From a 3-person startup to a 500-engineer org. Kubernetes-native architecture means adding capacity is infrastructure, not a rewrite. No performance cliffs.
Public status page with real uptime data. When something breaks, we post root cause analyses, not vague updates. We'd rather tell you the truth fast than look good slowly.
Continuous database backups with point-in-time recovery. Encrypted at rest and in transit. Your code and data survive anything short of the building burning down — and even then, the other region has it.
Real engineers, not chatbots. When your deploy is stuck at 2am, you'll talk to someone who can read the logs and fix it — not someone reading from a script.
Flashy demos get attention. Uptime gets trust. We care about both, but we know which one matters at 3am on a Friday.
The cloud SaaS is not a separate product — it's the same open source codebase, vetted, hardened, and operated by us so you don't have to.
Deploy on your own infrastructure. Full source code, AGPL-3.0 licensed. No vendor lock-in, no data leaves your network. You own everything.
The same open source codebase — vetted, hardened, and operated by the team that wrote it. Every release is tested, security-patched, and staged before it reaches your data.
No fork, no divergence. Cloud runs the same code you can audit on GitHub — we just make sure it's stable, patched, and running before you depend on it.
Replace the tool sprawl with a single workspace where planning, code, CI, and AI work together from day one.