Self-Hosted AI Agents for IT & Engineering Teams
Deploy a governed, self-hosted platform with private RAG over code, wikis, and runbooks, model routing, immutable audit logs & role-based access. Keep source code in-house — SOC 2 & ISO 27001 aligned at 40–60% lower AI cost.
The AI dilemma in software engineering
Engineering teams want AI assistance across code, docs, and incidents — but security and compliance won't allow proprietary source and customer data to flow into a third-party model. The result is shadow AI, or no AI at all.
Source-Code Confidentiality
Your codebase is core IP. Pasting it into a hosted assistant risks leakage, training on your code, and violating customer data-processing commitments.
Compliance Constraints
SOC 2 and ISO 27001 programs require data-residency, access control, and audit. Most hosted AI tools can't satisfy those controls out of the box.
Knowledge Fragmentation
Answers are scattered across repos, wikis, tickets, runbooks, and logs. Engineers waste hours hunting for context that should be one query away.
Shadow AI
When sanctioned tools don't exist, engineers use unapproved ones — moving code and data outside your control with zero visibility or audit.
Modern AI for engineers, inside your trust boundary
Data Sovereignty
Complete Data Sovereignty
Your code never leaves your network.
Deploy VDF AI entirely self-hosted, on-premises or in your private cloud. No external API calls. No source code, secrets, or customer data traveling to third-party servers — and nothing training an external model. Your codebase stays exactly where security requires it.
"Security finally said yes. The whole platform runs in our cluster — our code never touches a public model."
Inside your trust boundary
Compliance
Compliance & Governance Built-In
SOC 2 & ISO 27001 aligned from day one.
VDF AI provides the governance infrastructure security teams demand:
- Complete Audit Trails — every prompt, retrieval, tool call, and response logged for SOC 2 / ISO 27001 evidence
- Role-Based Access — scope agents and knowledge to teams, repos, and environments
- Read-Scoped Integrations — governed MCP access to repos, wikis, and tickets; changes require human approval
- Eliminate Shadow AI — give engineers a sanctioned, visible alternative
- Model Governance — track which models are used, when, and for what purpose
SOC 2 · ISO 27001 · EU AI Act
Cost Control
Intelligent Cost Management
Predictable AI spend across the org.
Engineering leaders need AI ROI without per-seat surprises. VDF AI delivers:
- Per-Operation Cost Tracking — know exactly what each task and team costs
- Model Routing Optimization — route routine queries to small models, reserve frontier models for hard problems
- Budget Controls — set limits by team, project, or environment
- ROI Reporting — tie AI assistance to cycle time, MTTR, and onboarding speed
- 40–60% Cost Reduction — compared to traditional cloud AI approaches
vs. hosted cloud alternatives
Use cases for IT & software engineering
Code Intelligence & Review
Agents that answer questions across your repos, explain unfamiliar code, and assist review — grounded in your actual codebase, never a public model.
Internal Documentation Q&A
Semantic search across wikis, design docs, and ADRs so engineers find the right context in seconds — fully cited to source.
Incident Response & Runbooks
During an incident, agents pull the relevant runbook, summarise recent changes and logs, and draft the postmortem — cutting time to resolution.
Ticket Triage & Support
Classify, enrich, and route tickets; draft responses grounded in docs and history — freeing on-call and support engineers for real work.
Docs & Test Generation
Draft documentation, changelogs, and test scaffolding from your code and specs — reviewed by engineers before merge.
Onboarding & Migration
Help new engineers ramp on a codebase, and assist large refactors or framework migrations with context-aware, auditable suggestions.
Technical specifications for engineering
| Requirement | VDF AI Capability |
|---|---|
| Deployment | Self-hosted on-premises, in your private cloud, Kubernetes, or air-gapped — inside your trust boundary |
| Code confidentiality | Source code & secrets stay in-house — no external API calls, no training on your code |
| Private RAG | Repos, wikis, design docs, runbooks & tickets stay on-premise inside your governed vector-store boundary |
| Role-based access | RBAC-scoped agents, tools & knowledge by team, repo & environment |
| Model routing | Tier-aware routing keeps routine queries on smaller models — frontier models reserved for hard problems |
| Audit logs | Immutable audit logs for prompts, retrievals, tool calls & responses — SOC 2 / ISO 27001 evidence & SIEM export |
| Integration examples | Git (GitHub / GitLab-style), Jira, Confluence, CI/CD & observability via governed, read-scoped MCP adapters |
| Encryption | At-rest and in-transit, customer-managed keys |
| Authentication | SSO, OIDC, LDAP, Active Directory, MFA |
| Uptime SLA | 99.9% (Enterprise tier) |
What changes after rollout
Questions engineering teams ask
How does VDF.AI keep proprietary source code out of public models?
VDF.AI is self-hosted: it runs inside your own infrastructure with no external API calls, so source code, secrets, and architecture never leave your network or train someone else's model. That removes the central objection to AI coding assistants for security-conscious engineering orgs — your codebase stays your codebase, with role-based access, immutable audit logs, and customer-managed encryption keys.
Is VDF.AI aligned with SOC 2, ISO 27001, and secure-SDLC requirements?
Yes. VDF.AI provides the audit trails, access controls, and data-residency guarantees that SOC 2 and ISO 27001 programs require, and it slots into a secure SDLC: every prompt, retrieval, tool call, and response is logged, access is role-scoped, and the platform deploys entirely within your trust boundary. It also supports the EU AI Act and GDPR controls relevant to internal AI use.
Can VDF.AI connect to our repos, Jira, Confluence, and observability tools?
Yes, through governed MCP integrations. Agents can search across Git repositories, internal wikis, ticketing, runbooks, and logs to answer engineering questions, draft documentation, and assist with incident response — with read-scoped, audited access and humans approving any change that lands in your systems.
Why self-hosted AI instead of a hosted cloud coding assistant?
Hosted coding assistants require sending code context to third-party infrastructure, which conflicts with source-code confidentiality, customer data-processing commitments, and many SOC 2 / ISO 27001 controls. Self-hosted AI keeps code, tickets, and internal knowledge inside your boundary — no third-party access, no training on your code, no surprise terms-of-service changes — while still giving engineers modern AI assistance.
Ready to give engineers AI without the leak risk?
Talk to our team about your code, knowledge, and compliance requirements.