Same automation. Four different workflows.
Tuesday you saw the code. Today you see how analysts, portfolio managers, risk officers, and operations teams actually use it. Each role has different needs, different views, different wins.
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 finally have time to think about what the data means, not just collect it."
— Senior Analyst, 6 years buy-side
How Teams Work Together Through the System
2pm: Fed announces surprise rate hike. See how all four roles collaborate through automation to respond in 15 minutes.
Fed Rate Decision - Real-Time Response
✨ Scroll here to watch the workflow
Practice-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Report Turnaround | 4 hours average | 30 minutes average | 87% faster |
| Data Accuracy | 92% (manual errors) | 99.8% (AI validation) | 8.5% improvement |
| Analyst Capacity | 3-4 reports/day | 12-15 reports/day | 4x throughput |
| Compliance Breaches | 2-3 per month | 0 per month | 100% reduction |
Getting Your Team On Board
Analysts think AI will replace their judgment
Show side-by-side: AI handles data aggregation, analysts focus on interpretation and strategy. AI is research assistant, not decision-maker.
Frame as 'upgrade your role' not 'automate your job'. Analysts become strategists, not data collectors.
Portfolio managers don't trust AI-generated insights
Run parallel for 30 days: manual process + AI. Track accuracy, speed, and edge cases. Show AI catches things humans miss (overnight news, correlation shifts).
PMs see AI as 'always-on analyst' that never sleeps. Trust builds through data, not promises.
Risk officers worried about regulatory approval
Provide audit trail: every AI decision is logged, explainable, and traceable. Show compliance documentation and SOC2 certification.
Risk team becomes early adopter once they see transparency. They use it to prove compliance, not hide it.
Operations concerned about upfront cost and disruption
Calculate ROI: $8,500/month subscription saves $42K/month in analyst time = 6-day payback. Pilot with 5 users, scale after proof.
Show monthly savings chart. Decision becomes obvious when ROI is 5x in first month.
Team resists learning new tools mid-quarter
Implementation during low-volume period (July/August). 2-week training with hands-on sessions. AI mimics existing workflows, not radical change.
Adoption in 3 weeks. Team reports 'easier than learning Bloomberg terminal' and sees immediate time savings.
Investment & ROI
Typical payback in 6-10 days through analyst time savings
Pricing
ROI Calculator
Proven Results
From Demo to Live in 3 Weeks
From demo to production in just 3 weeks
- Connect data sources (Bloomberg, FactSet, internal databases)
- Configure role-specific dashboards (analyst, PM, risk, ops)
- Import historical reports for AI training baseline
- Train each role on their workflow (3hr sessions per role)
- Run pilot with 5 users (1 from each role)
- Gather feedback, adjust templates and alerts
- Roll out to all users with role-based access
- Daily check-ins for first week (15-min standups)
- Measure baseline metrics vs new performance
Enterprise deployments take 6-8 weeks for custom AI models and regulatory reviews
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