On-prem AI for Your Codebase
OnPremize is an enterprise AI platform that runs entirely inside your network. It combines code-aware RAG retrieval and LoRA fine-tuning to answer questions about your codebase with cited sources.
Enterprise Engineering Pain
Real problems that slow engineering teams down.
Extended Onboarding Time
New engineers spend weeks navigating unfamiliar codebases before becoming productive.
Tribal Knowledge
Critical context lives in the heads of senior engineers, creating bottlenecks and risk.
PR Throughput
Code reviews stall as reviewers struggle to understand cross-cutting concerns.
Compliance Constraints
Regulated environments in finance, healthcare, and government require data to stay on-premise.
Tool Sprawl
Disconnected AI tools fragment workflows and create security gaps.
How It Works
Four steps to code intelligence without data leaving your network
Connect → Index → Retrieve → Answer/Act with full citations
Connect Sources
Securely connect repositories and access-controlled sources inside your network.
Index
We build a private index so your code and history are searchable and grounded.
Retrieve
Queries pull the most relevant context with clear citations for verification.
Answer & Act
Deliver grounded answers with citations back to your source code.
What Gets Indexed
Code Repositories
Your entire codebase becomes searchable context
- Monorepos and multi-repo setups
- Internal packages and dependencies
- 40+ supported languages and file types
Git History
Understand why code changed, not just what changed
- Commit messages and intent
- Blame and ownership context
- Diffs and code evolution
Internal Documentation
Surface institutional knowledge from existing docs
- READMEs and ADRs
- Runbooks and playbooks
- Architecture decision records
Jira Issues + PRs
Training signals from your development workflow
- Issue context and requirements
- Merged PR patterns
- Code review feedback
Fine-Tune to Your Standards
Optional local fine-tuning that adapts the model to match your internal conventions
Training Pipeline
Build custom LoRA adapters from your own codebase. Fine-tuning draws on the same sources indexed in the retrieval pipeline — your code, Git history, and documentation.
Read the LoRA fine-tuning platform details
Lightweight adapter that runs alongside your base model, adding your organization's knowledge without modifying the original weights.
Why Fine-Tune?
- Model learns your internal conventions and patterns
- Suggestions match your codebase style automatically
- Fine-tuning runs entirely on your infrastructure
- No training data leaves your network boundary
Example: Learning from commits
Training data never leaves your infrastructure. Models are fine-tuned locally on your GPUs with no external API calls.
Modules
Code Assistant
Ask questions about your codebase and get grounded answers with citations. Every response includes file paths and line ranges so engineers can verify quickly. Runs inside your network.
Benefits
- Faster onboarding and fewer "who knows this?" interrupts
- Higher trust via cited answers with file + line ranges
- Better suggestions that match internal patterns and conventions
Key Features
- Hybrid retrieval with citations
- Uses Git history to explain why code changed
- OpenAI-compatible and Anthropic-compatible API
- VS Code extension (Coming Soon)
- Slack integration for team Q&A (Coming Soon)
Example Use Case
"Where is authentication handled for payment-api requests?" → returns a 3–7 bullet answer with citations like [src/auth/... L120–L180].
Security & Deployment
Designed for regulated environments. Your data never leaves your boundary.
Data Residency
- No data egress — all processing stays on-premise
Data Protection
- Secrets and PII redaction (Coming Soon)
- Encryption in transit (TLS)
- Encryption at rest (configurable) (Coming Soon)
Operational Controls
- Path allowlists / denylists (Coming Soon)
- Read-only mode option (Coming Soon)
- Approval gates for actions (Coming Soon)
- Audit logging with SIEM export (Coming Soon)
Deployment Options
- Kubernetes (Coming Soon)
- Docker Compose (pilot environments)
- VM-based (Coming Soon)
Operational Model
- Versioned releases with rollback (Coming Soon)
- Offline artifact bundles (Coming Soon)
- Reference architectures for HA (Coming Soon)
OnPremize is designed for regulated environments including finance, healthcare, and government. It can be deployed within regulated environments with controls scoped to your security requirements.
Choose Your Package
Each package builds on the one before it. Start with code intelligence, grow into automation.
Team
Code-aware AI for your engineering team
- Code Assistant with cited answers
- Hybrid retrieval across your repositories
- OpenAI-compatible and Anthropic-compatible API
- Standard onboarding and integration support
- Community Slack access
Business
Organizational knowledge and custom-trained models
- Everything in Team
- Knowledge Base — auto-generate architecture Q&A
- LoRA fine-tuning on your code patterns
- Priority support SLA
- Dedicated onboarding engineer
Enterprise
AI-assisted changes with quality governance
- Everything in Business
- Change Automation — reviewable patch proposals (Coming Soon)
- Quality Assurance — regression packs and quality gates (Coming Soon)
- Custom model advisory and training consultation
- Executive review cadence and success metrics
Frequently Asked Questions
Common questions about security, deployment, and operations
Deploy on-prem. Keep control. Ship faster.
Enterprise code intelligence that runs entirely inside your infrastructure. VPC-ready. Air-gap compatible. Open-weight models.