What is proxifai?
An introduction to proxifai — one platform to plan, build, ship, and operate software
proxifai is a unified software development platform — an internal developer platform that spans the whole lifecycle: plan, code, deploy, and operate, with AI woven through every step. Project management, Git hosting, CI/CD, deployments to Kubernetes, observability, cost, and AI agents all share one data model. When everything shares one data model, AI finally has the context to do real work.
The Problem
A typical engineering team stitches together 10+ disconnected tools — Jira, GitHub, Jenkins, Argo CD, Confluence, Datadog, PagerDuty, Kubecost, and a growing list of AI copilots. Context is scattered across every seam. No single tool sees the full picture, so AI assistants can only operate in silos.
The Solution
proxifai unifies the entire lifecycle around a single noun — the service — and groups the work into a few pillars:
- Plan — Issues, sprints, roadmaps, initiatives, and requests, connected directly to your code.
- Code — Native Git hosting with pull requests, code review, releases, and an OCI container registry.
- Deploy — Ship a service to real Kubernetes via GitOps: environments, per-PR preview environments, and variables/secrets.
- Workflows — Container-isolated automation graphs (CI/CD workflows and event-driven automations) with triggers, approvals, and AI agent steps.
- Infrastructure — Managed Postgres, Kafka, and cache, plus isolated virtual clusters.
- Operate — Insights (logs, metrics, traces, dashboards), Alerts (on-call incidents and rules), and Cost (FinOps across every kind of spend).
- AI — Agents that run in isolated containers, a Knowledge Base for retrieval over your own data, and a Models gateway to every major LLM provider.
Core Capabilities
| Capability | What It Does | Replaces |
|---|---|---|
| Project Management | Issues, sprints, roadmaps, initiatives, requests, teams | Jira, Linear, Asana |
| Git Forge | Repository hosting, PRs, code review, releases, OCI registry | GitHub, GitLab, Bitbucket |
| Workflows & CI/CD | YAML workflows and automation graphs, secrets, approval gates, runners | GitHub Actions, Jenkins, n8n |
| Deploy / PaaS | GitOps deploys to Kubernetes, environments, preview environments, secrets | Vercel, Heroku, Argo CD |
| Managed Infrastructure | Postgres, Kafka, cache, isolated clusters | RDS, Confluent, ElastiCache |
| Observability | Logs, metrics, traces, and dashboards | Datadog, Grafana |
| Alerting & On-call | Incidents, alert rules, acknowledge/resolve | PagerDuty, Opsgenie |
| Cost / FinOps | Unified spend, allocation, budgets, optimization | Kubecost, CloudHealth |
| AI Models Gateway | One API to OpenAI / Anthropic / Google plus BYOK, at list price | Direct provider APIs |
| AI Agents & Knowledge Base | Container agents and retrieval with full project context | ChatGPT, siloed copilots |
| Documents & Inbox | Collaborative specs and unified, actionable notifications | Confluence, Notion |
How It Works
proxifai organizes work into organizations and teams. Inside a team you build services — the thing you plan, code, deploy, and operate. Across the app, the service is a scope filter rather than a separate place: pick a service from the top bar and every page (issues, deployments, logs, cost) narrows to it.
Within a service you create issues that connect to branches and pull requests in the built-in forge, deploy across environments (dev → prod, plus auto-expiring preview environments per PR), and watch it run through logs, alerts, and cost. AI agents run in isolated containers and can triage issues, write code, run tests, and open PRs — all with full context across your organization.
Your AI chat can reference a sprint, trace it to the PRs that implement it, read the CI output, check the deploy that shipped it, and see the alert it fired — all in one query. No copilot can do this across fragmented tools.
Who Is It For?
- Development teams that want to stop context-switching between 10 tools
- Platform and DevOps teams that want plan → deploy → operate on one substrate
- Engineering leaders who need visibility across planning, code, delivery, and spend
- Teams building with AI that need agents with full organizational context
- Organizations that want one audit trail, one bill, and unified governance