Same governance platform. Four different daily workflows.
Tuesday you saw the automation code. Today you see how real team members use it. Each role monitors different risks, enforces different policies, and makes different decisions.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Workflow Process
Impact By The Numbers
"I went from firefighting violations to preventing them. The system catches risks I'd never see manually."
— Governance Manager, 6 years enterprise AI
How Roles Work Together on High-Risk Requests
Watch how the system routes this high-risk request through 4 roles in 18 minutes (vs 3 days manually).
Engineering team requests GPT-4 access for customer data analysis
✨ Scroll here to watch the workflow
Practice-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Policy Violations | 12-15 monthly | 0-1 monthly | 93% reduction |
| Request Approval Time | 3.2 days average | 18 minutes average | 99.6% faster |
| Governance Coverage | 5% spot-check | 100% automated | 20x coverage |
| Monthly Cost Overruns | $18K unplanned | $400 exceptions | 97.8% reduction |
Getting Your Team On Board
Managers think automation will slow down innovation
Show speed data: 18 min approval vs 3 days. Fast AND safe, not either/or.
Frame as 'unblocking teams faster' not 'adding more gates'. Innovation accelerates.
Analysts worried AI will miss nuanced risks
Run parallel for 30 days: AI + human review. Show AI caught 23 risks humans missed.
Analysts see AI as 'second pair of eyes' that never gets tired. Trust builds through data.
Engineers think governance will block their work
Show self-service portal: 90% of requests auto-approved in <1 min. Only 10% need review.
Engineers get faster access than before. Governance becomes enabler, not blocker.
Operations worried about implementation complexity
Pilot with 1 team, 1 model (GPT-4), 2 weeks. Prove value before company-wide rollout.
Small win builds confidence. Expand to 5 models, then 15, then all LLMs over 8 weeks.
Executives concerned about upfront cost
Calculate ROI: $8,500/month savings = 18-day payback period on $5K/month subscription.
Show monthly savings chart. Decision becomes obvious when payback is under 3 weeks.
Investment & ROI
Typical payback in 18-25 days through time savings and violation prevention
Pricing
ROI Calculator
Proven Results
From Demo to Live in 4 Weeks
From demo to production in just 3 weeks
- Connect LLM providers (OpenAI, Anthropic, etc)
- Import existing policies and rules
- Configure role-based dashboards
- Set up cost tracking and attribution
- Map your policies to automation rules
- Define risk thresholds and escalation paths
- Configure approval workflows by risk level
- Set up alerting and notifications
- Run pilot with 1 team, 3 models
- Train each role on their workflows (2hr sessions)
- Parallel run: automation + manual review
- Gather feedback, tune policies
- Roll out to all teams and models
- Daily check-ins for first week
- Measure baseline metrics vs new performance
- Document wins and edge cases
Enterprise deployments may take 6-8 weeks for custom integrations (SSO, SIEM, data residency)
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