Self-hosted AI coding assistant and LLM gateway for your own infrastructure
An enterprise AI coding platform your security team will actually approve. LLM gateway, codebase intelligence, AI code audit server, and custom skills — all deployed inside your own infrastructure. Your source code stays in your network.
Everything in the platform
Each component solves a specific gap in enterprise AI coding. Deploy all four or start with the one that unblocks your team first.
LLM Gateway
A self-hosted API gateway that proxies all LLM requests from your engineering tools — routing, rate-limiting, cost tracking, and access control across every model your team uses.
Codebase Intelligence
Semantic search and AI-powered understanding across your entire codebase. Ask questions about how systems work, find usages, trace call chains — a Sourcebot alternative built for enterprise scale.
AI Code Audit Server
Automated AI-powered code review that runs in your CI/CD pipeline. Catches security vulnerabilities, logic errors, dependency risks, and style violations using Codex, Claude, or your own model.
Custom Coding Skills
Extend your AI coding assistant with domain-specific skills — your internal API patterns, migration playbooks, security rules, and architecture constraints baked into every suggestion.
Usage & Cost Intelligence
Track every LLM request by team, project, and engineer. Understand AI ROI, catch runaway costs, and enforce per-team quotas — all from the gateway dashboard.
IDE & CLI Integration
Works with VS Code, JetBrains, Neovim, and the CLI. Engineers keep using the tools they know — the platform runs silently behind your existing workflow.
How the platform is built
Three self-contained layers that stack together into a complete enterprise AI coding platform — deployed inside your infrastructure from day one.
LLM Gateway & Request Routing
All LLM traffic from your IDE plugins, CI pipelines, and internal tools routes through a single self-hosted gateway. Route requests to different models based on task type, cost, or team policy — with full logging of every request, token count, and response.
Codebase Intelligence Engine
Indexes your entire codebase with code-aware embeddings. Engineers can ask “how does the payment flow work?” or “where is this interface implemented?” and get accurate, cited answers — plus semantic code search across millions of lines.
AI Code Audit & Custom Skills
An LLM-powered audit server that runs in your CI/CD pipeline. Integrates with GitHub Actions, GitLab CI, and Jenkins. Custom skills encode your team’s architecture decisions, security rules, and domain patterns — so every pull request gets reviewed against your actual standards.
Built for teams where code cannot leave the building
Financial services, defence contractors, healthcare platforms, and IP-sensitive companies — if your security policy says no external AI for code, this is your answer.
On-Premise Deployment
Every component runs on your own hardware or private data centre. No internet egress required during normal operation.
Private Cloud (Your Account)
Deploy inside your own AWS, Azure, or GCP account with VPC isolation. No cross-tenant data access. You own the infrastructure.
Air-Gapped Mode
Runs fully offline with locally hosted models (Llama, Mistral, CodeLlama) for environments with no external network access.
All Requests Logged Locally
Every LLM request, code snippet, and response is logged in your environment. Full audit trail for compliance and security review teams.
Secrets & PII Filtering
Automatic scanning and redaction of secrets, API keys, and PII before any content reaches the LLM layer — even in self-hosted deployments.
RBAC & Quota Management
Per-team and per-engineer access policies. Set token quotas, restrict model access, and enforce code scope boundaries by repository.
On-Premise
Your servers, fully offline
Private Cloud
Your VPC, your account
Air-Gapped
Local models, no internet
Hybrid
Local gateway, cloud models
| LLM Gateway Protocol | OpenAI-compatible REST API — drop-in replacement for existing tooling |
| Supported LLMs | OpenAI, Anthropic, Meta Llama, Mistral, CodeLlama, DeepSeek Coder, or your own model |
| IDE Support | VS Code, JetBrains (IntelliJ, PyCharm, GoLand), Neovim, CLI |
| SCM Integration | GitHub, GitLab, Bitbucket, Azure DevOps |
| CI/CD Integration | GitHub Actions, GitLab CI, Jenkins, CircleCI, custom webhooks |
| Codebase Index Scale | Multi-repo and monorepo support; scales with your infrastructure |
| Audit Output Format | SARIF, JSON, GitHub/GitLab native annotations |
| Deployment Time | Gateway live in 1 week; full platform in 30 days |
Why enterprises choose Origins AI over Copilot and Sourcebot
GitHub Copilot and Cursor are great developer tools — but they send your code to GitHub’s or Cursor’s cloud servers. Origins AI keeps everything inside your firewall.
| Capability | Origins Coding Tool | GitHub Copilot | Sourcebot | Cursor |
|---|---|---|---|---|
| Fully on-premise deployment | ✓ | ✗ | ✓ | ✗ |
| Code never sent externally (on-premise & air-gapped modes) | ✓ | ✗ | ✓ | ✗ |
| LLM gateway with routing | ✓ | ✗ | ✗ | ✗ |
| AI code audit in CI/CD | ✓ | LIMITED | ✗ | ✗ |
| Codebase Q&A / semantic search | ✓ | LIMITED | ✓ | ✓ |
| Custom domain skills | ✓ | ✗ | ✗ | ✗ |
| BYO / local model support | ✓ | ✓ | ✓ | LIMITED |
| Per-team usage & cost tracking | ✓ | BASIC | ✗ | ✗ |
Built by engineers who’ve shipped AI at Amazon, Sony, and Samsung
We built this because we faced the same problem — great AI coding tools that security would never approve. So we built the enterprise version.
Built for Security Teams
Self-hosted and air-gap capable, with full audit trails. Designed to meet the requirements of enterprise security review — no code sent to external services in on-premise mode.
One Gateway, All Models
Route to OpenAI, Anthropic, Meta Llama, or CodeLlama from one interface. Switch models without changing a line of tooling config.
Knows Your Codebase
Semantic search and AI Q&A trained on your specific repositories — not generic open-source knowledge.
Encodes Your Standards
Custom skills capture your architecture patterns, security rules, and migration playbooks — so AI suggestions match how your team actually builds.
Measurable ROI
Track time saved per engineer, PR cycle reduction, and audit catch rate — so you can prove the value to engineering leadership.
Gateway Live in 1 Week
The LLM gateway can be live and routing traffic from your IDE plugins within a week. Full platform in 30 days.
Ready to deploy AI coding tools your security team approves?
Book a 45-minute technical demo. We’ll walk through the architecture, show you a live deployment, and scope a 30-day pilot starting with the LLM gateway.
Frequently Asked Questions
Does our source code leave the network when using the Coding Tool?
In on-premise and air-gapped deployment modes, no source code is sent to any external service. All LLM requests are routed through your self-hosted gateway, and all model inference runs on your own infrastructure. In hybrid mode (local gateway, hosted model), only the specific code context submitted to the model leaves your network.
Can the platform run in an air-gapped environment?
Yes. In air-gapped mode, the platform runs locally hosted models such as Meta Llama, Mistral, or CodeLlama — with no internet connection required. This mode is suited for defense, government, and financial institutions with strict network isolation requirements.
Which IDEs and editors are supported?
The Origins AI Coding Tool supports VS Code, JetBrains IDEs (IntelliJ, PyCharm, GoLand), Neovim, and the CLI. Engineers use the tools they already know — the platform routes their AI requests through your private gateway without changing their workflow.
How does the LLM gateway work?
The gateway exposes an OpenAI-compatible REST API, so any tool that currently calls OpenAI can be pointed at your private gateway with a single configuration change. It routes requests to whichever models you have configured, logs every request and token count, and enforces per-team rate limits and quotas.
What CI/CD systems does the AI code audit integrate with?
The audit server integrates with GitHub Actions, GitLab CI, Jenkins, and CircleCI via webhooks. Results are returned in SARIF format and as native PR annotations on GitHub and GitLab, so findings appear inline in pull request reviews.
How long does it take to get the platform running?
The LLM gateway can be live and routing traffic from your IDE plugins within one week. The full platform — including codebase intelligence indexing and the audit server in CI/CD — is typically running within 30 days.