Move enterprise AI from pilots to production with control over data, models, actions, and cost.
Disconnected AI tools create more integrations to maintain, more policies to manage, and less clarity on results. Scaling enterprise AI requires a shared way to control how models, agents, and applications operate.
This whitepaper explains how AISquared’s UNIFI brings these capabilities together through an enterprise AI control plane, connecting business data, AI workflows, and applications with governance built into execution.
What You’ll Learn
- Reduce AI costs: Use policy-based AI model routing to match routine tasks with efficient models and reserve larger models for complex work.
- Put AI governance into practice: Apply access controls, guardrails, and human approvals before AI takes action.
- Connect AI to daily work: Ground responses in approved enterprise data and deliver results inside business applications.
- Build governed AI applications: See how UNIFI’s App Builder turns business needs into applications with data, storage, and deployment controls.
- Plan for secure deployment: Explore options for customer VPC, self-hosted, and air-gapped environments.
- Measure business impact: Track quality, cost, adoption, and workflow outcomes to assess enterprise AI ROI.
A Framework for Enterprise AI Control
Explore AISquared’s seven-layer AI Controls Framework and how UNIFI applies it across data access, context, workflow orchestration, governance, delivery, and observability.
Use the guide to identify gaps in your AI setup and plan a first deployment with a clear owner, defined controls, and measurable results.
