Uncontrolled routing
Work moves between models, tools, and people without a durable record of why each step happened or who approved it.
Enterprise AI Systems Integrator
End AI pilot purgatory.
Your organization already has LLM experiments. What is missing is the control plane around them — routing, validation, auditability, escalation, and integration into the operating systems your business already runs on.
The problem
LLM pilots fail when governance is bolted on after the fact.
Pilot purgatory is not a model problem. It is a control problem: routing, evidence, and integration treated as optional. We treat them as the product.
Unverified output
Teams cannot demonstrate what was reviewed, what was rejected, or what evidence supports the final answer.
Unintegrated workflow
Useful AI output stays outside ticketing, support, security, and operational systems — and therefore outside the business.
What MGV builds
The operating layer around enterprise AI.
Three pillars. Each one a procurement-ready engagement. Each one rests on the same posture: fail-closed by default, evidence required to advance.
i.
Pillar 01 · Routing
Governed AI Dispatch & Routing
Convert ad-hoc AI usage into controlled work lanes with model selection, role constraints, escalation paths, and durable handoff records.
- Agent & tool routing rules
- Human-in-the-loop checkpoints
- Evidence-backed lane state
- Reversible escalation paths
ii.
Pillar 02 · Validation
Defense-in-Depth Validation
Wrap model output in independent review, rendered browser evidence, policy checks, and release gates before the work becomes operational truth.
- Adversarial peer review
- Rendered-screen UAT evidence
- Claim & compliance hardening
- Fail-closed release gates
iii.
Pillar 03 · Integration
Enterprise Legacy Integration
Connect AI workflows into contact-center, firewall, ticketing, CRM, audit, and procurement realities — not novelty demos around the model.
- Existing-system mapping
- Operational runbooks
- Reversible deployment paths
- Audit-aligned telemetry