Same automation. Four different workflows.
Tuesday you saw the code. Today you see how real team members 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 went from drowning in updates to actually building relationships."
— IR Manager, Series B SaaS, 6 years fundraising
How Roles Work Together Through the System
Sequoia partner hasn't responded to last 2 updates. System detects pattern, team collaborates to re-engage.
High-Priority Investor Follow-Up
✨ Scroll here to watch the workflow
Team-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Weekly Team Hours on IR | 75 hours (Manager 40 + Analyst 20 + Ops 15) | 14 hours (Manager 8 + Analyst 4 + Ops 2) | 81% reduction |
| Investor Relationships Managed | 50 active (capacity limit) | 150 active (3x scale) | 200% increase |
| Update Response Rate | 22% (investors ignore generic updates) | 38% (personalized, relevant content) | +73% engagement |
| Time to Answer 'What Did We Say?' | 10-15 min (search old emails) | 5 seconds (AI semantic search) | 99% faster |
Getting Your Team On Board
IR Manager: 'AI can't write like me, investors will know it's automated'
Show side-by-side: AI draft vs their draft. AI uses their past emails as style guide. 90% of the time, they can't tell which is which. Plus, they edit every draft before sending - it's a starting point, not a replacement.
After 2 weeks: 'I spend 5 minutes editing instead of 20 minutes writing from scratch. My voice is still there.'
IR Analyst: 'I'll lose my job if AI does the data work'
Reframe: 'You're getting promoted from data entry to strategic analysis.' Show them time saved (16 hours/week) can be spent on competitive intelligence, investor mapping, deal sourcing - the work that gets you promoted.
Analyst becomes investor intelligence specialist, gets raise after 6 months.
Operations Lead: 'What if the system makes a mistake and sends wrong info to investors?'
Show approval workflow: AI drafts, humans review, nothing sends without explicit approval. Plus audit log tracks every change. More control than current process where anyone can send anything.
After 30 days: 'I trust this more than our old process. I can see exactly what went out and when.'
CEO: 'This costs too much for a 'nice to have' tool'
Show ROI: Team saves 61 hours/week = $4,880/month at $80/hour blended rate. System costs $2,000/month. Net savings: $2,880/month = $34,560/year. Pays for itself in 2 weeks.
CEO approves pilot: 'If we save even half that, it's worth it.'
Team: 'We're too busy to learn a new system right now'
Run parallel for 2 weeks: Keep doing current process, AI runs in background. Compare results. Team sees AI-generated summaries match their manual notes in 5 minutes vs 2 hours. Adoption becomes obvious.
After parallel run: 'Why are we still doing this manually?' Team self-adopts.
Investment & ROI
Typical payback in 2-3 weeks through time savings
Pricing
ROI Calculator
Proven Results
From Demo to Live in 3 Weeks
From demo to production in just 3 weeks
- Connect email (Gmail/Outlook), calendar, CRM
- Import historical investor data & interactions
- Configure team roles & permissions
- Set up custom email templates
- Train AI on your past communications (style learning)
- Role-specific training: Manager (1hr), Analyst (1hr), Ops (30min)
- Run parallel: AI drafts alongside manual process
- Team tests AI summaries, drafts, dashboards
- Gather feedback, adjust AI parameters
- Refine workflows based on real usage
- Switch from parallel to primary workflow
- AI handles all new interactions automatically
- Daily check-ins for first week (30 min)
- Measure baseline: time saved, quality, team satisfaction
- Optimize based on week 1 metrics
Enterprise deployments take 4-6 weeks for custom integrations, compliance reviews, and multi-team rollout.
2026 Randeep Bhatia. All Rights Reserved.
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