New VDF AI Enterprise is now available on AWS Marketplace
IT & Software Engineering Agentic OS Control Plane

Engineering AI agents that turn delivery objectives into governed execution

VDF.AI sits above repos, Jira, Confluence, GitBook, CI/CD, observability, incidents, tickets, and runbooks. State the engineering objective and the OS activates the right agents, code tools, approval gates, and audit trail while keeping source code inside your trust boundary.

Explore Engineering Workflows
6Engineering workflows across code, incidents, docs & migration
0Repo, Jira, wiki, CI/CD or observability migrations required
100%Self-hosted or sovereign cloud for source code
Every runPermissioned, reviewable, costed and audit logged
Built for secure engineering
SOC 2 ISO 27001 EU AI Act GDPR Secure SDLC Self-Hosted
Engineering control plane

Code and operations intelligence in. Governed control plane. Engineering execution out.

VDF.AI sits above repos, Jira, Confluence, GitBook, CI/CD, observability, incidents, runbooks, and ticketing systems. It gives engineers governed AI across the software lifecycle without sending source code to a hosted model.

01 Intelligence in 02 Control plane 03 Agents and tools 04 Execution out

Intelligence in

Live governed execution

VDF AI - Engineering - Model agnostic - Self-hosted - Any LLM

IT and Software Engineering Agentic OS Control Plane

Objective engine, source-code controls, secure-SDLC guardrails, agent registry, memory, and feedback across code, tickets, docs, incidents, and migrations.

Orchestration

Engineering agent router

Routes work by repo, service owner, risk class, environment, change type, incident severity, and reviewer path.

SOC 2 evidenceSecure SDLCRepo RBACChange approval
Record Keeping Agent Audit TrailExecution Trace

Execution out

From objective to outcome

How IT and engineering operations transform

The control plane starts from an engineering outcome and assembles the code, docs, tickets, checks, agents, and approvals needed to deliver it.

  1. 1

    Your engineering stack stays

    GitHub, Jira, Confluence, GitBook, CI/CD, observability, incident tools, and runbooks remain the working systems. VDF.AI coordinates above them.

    No workflow migration
  2. 2

    The engineering objective builds the plan

    Cut MTTR, review a PR, generate tests, document an API, migrate a service, or onboard a new engineer. The OS returns a plan by repo, risk, and approval path.

    Outcome-led execution
  3. 3

    Agents operate inside source-code policy

    Code, DevOps, documentation, planning, and support agents run with repo permissions, approval gates, secret handling, and secure-SDLC checks.

    Governed autonomy
  4. 4

    Every run becomes engineering memory

    Accepted review patterns, incident lessons, docs, and architecture decisions compound instead of disappearing in chat history.

    Traceable learning loop
The Industry Challenge

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.

01

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.

02

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.

03

Knowledge Fragmentation

Answers are scattered across repos, wikis, tickets, runbooks, and logs. Engineers waste hours hunting for context that should be one query away.

04

Shadow AI

When sanctioned tools don't exist, engineers use unapproved ones — moving code and data outside your control with zero visibility or audit.

The VDF AI Solution

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."

100%
Self-Hosted Deployment

Inside your trust boundary

Air-gap readyCustomer-managed keysNo training on your code

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
Audit-grade
Compliance Ready

SOC 2 · ISO 27001 · EU AI Act

Immutable logsSIEM exportRead-scoped access

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
40–60%
Cost Savings

vs. hosted cloud alternatives

Per-op trackingTier-aware routingBudget guardrails
Where it pays back

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.
VDF AI agents and tools

Improve engineering workflows with agent-tool networks

Each workflow combines a focused engineering agent pattern with the tools needed to read code, retrieve docs, analyze incidents, generate deliverables, request approval, and preserve the audit trail.

Engineer assist

Code intelligence and review

Answer codebase questions, review PRs, map dependencies, and flag risky changes without sending source to hosted tools.

Code Review Agent
GitHub SearchAI Code ReviewPR Review Assistant
Explore workflow Docs grounded

Internal documentation Q&A

Search wikis, runbooks, ADRs, and code-linked docs with citations and team-level permissions.

Enterprise Search Agent
Confluence SearchGitBook SearchCitation Verify
Explore workflow On-call assist

Incident response and runbooks

Correlate alerts, recent changes, runbooks, and ownership to prepare incident actions and postmortems.

DevOps Advisor Agent
Anomaly DetectReport CompileExecution Trace
Explore workflow Queue assist

Ticket triage and support

Classify, enrich, summarize, and route tickets using Jira, docs, and historical resolutions.

Development Planning Agent
Jira SearchJira Issue InsightsSentiment Analysis
Explore workflow Engineer approved

Docs and test generation

Draft tests, docs, changelogs, and API references from code and specs for engineer review.

Writing Assistant Agent
Document GeneratorAPI Docs GeneratorREADME Generator
Explore workflow Guided migration

Onboarding and migration

Guide new engineers through repositories and plan refactors with architecture and dependency evidence.

Code Architect Agent
Repository MapArchitecture InferenceChange Impact
Explore workflow
Under the hood

Technical specifications for engineering

RequirementVDF AI Capability
DeploymentSelf-hosted on-premises, in your private cloud, Kubernetes, or air-gapped — inside your trust boundary
Code confidentialitySource code & secrets stay in-house — no external API calls, no training on your code
Private RAGRepos, wikis, design docs, runbooks & tickets stay on-premise inside your governed vector-store boundary
Role-based accessRBAC-scoped agents, tools & knowledge by team, repo & environment
Model routingTier-aware routing keeps routine queries on smaller models — frontier models reserved for hard problems
Audit logsImmutable audit logs for prompts, retrievals, tool calls & responses — SOC 2 / ISO 27001 evidence & SIEM export
Integration examplesGit (GitHub / GitLab-style), Jira, Confluence, CI/CD & observability via governed, read-scoped MCP adapters
EncryptionAt-rest and in-transit, customer-managed keys
AuthenticationSSO, OIDC, LDAP, Active Directory, MFA
Uptime SLA99.9% (Enterprise tier)
ROI Snapshot

What changes after rollout

−45%
Mean time to resolve incidents
40–60%
Lower AI operating costs vs. cloud
10×
Faster knowledge retrieval
−50%
New-engineer onboarding time
FAQ

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.

Contact Sales

Or try VDF AI in the cloud →