The Company: ProjectFlow
ProjectFlow is a B2B project management SaaS serving over 3,000 small teams globally. Founded in 2023, they grew quickly — but their support team didn't keep pace with their user base.
By late 2025, they had:
- 3,200 active workspaces
- 2 full-time support agents
- An average ticket response time of 4.2 hours
- A churn survey showing 22% of churned users cited "poor support" as a factor
- True bugs — behavior that doesn't match documentation (needs dev involvement)
- Billing disputes — requires human judgment and payment provider access
- Account recovery — lost 2FA, corporate account reassignment (security protocols)
- Feature requests — users who want something new (forwarded to product team)
- Angry users — the AI hands off immediately when tone shifts to frustration
The problem wasn't their product — it was their support economics.
The Support Math Problem
Each of their two support agents handled an average of 65 tickets per day. At 250 working days per year, that's roughly 32,500 tickets per year — at a fully-loaded cost of approximately $90K per agent.
Total support cost: ~$180,000/year for two agents.
When they audited the ticket types, the breakdown was stark:
| Ticket Category | Volume | % of Total |
|---|---|---|
| "How do I do X?" (feature questions) | 38% | 12,350/yr |
| Integration setup help | 22% | 7,150/yr |
| Billing & plan questions | 18% | 5,850/yr |
| Bug reports | 12% | 3,900/yr |
| Account access issues | 10% | 3,250/yr |
| Metric | Before Callsup | After 30 Days |
| Daily tickets to humans | 130 | 13 |
| AI containment rate | — | 90% |
| Avg. response time | 4.2 hours | < 45 seconds |
| Agent capacity freed | — | 90% |
| Monthly support cost | ~$15,000 | ~$2,800 |
| User CSAT (support) | 3.4 / 5 | 4.5 / 5 |
| Churn citing "poor support" | 22% | 7% (Month 3) |
90% containment meant that 90% of tickets were resolved by the AI without human involvement. Their two agents now handled 13 tickets per day — all of which were genuine tier-2 issues: bugs, billing disputes, account recovery.
The Compounding Effects
The immediate cost savings ($180K → $34K annually) were striking. But the second-order effects mattered just as much:
1. Better support coverage, globally
ProjectFlow serves users across 11 time zones. Previously, tickets from Asia-Pacific users sat for 8+ hours before anyone in their India-based support team arrived. Now the AI handled those queries instantly, 24/7.
2. Product insights from AI conversations
Callsup logged every conversation. ProjectFlow's product team started mining these logs weekly — seeing which features confused users most, which integrations had undocumented edge cases, and which requests came up repeatedly. This shaped their Q2 roadmap more than any user interview had.
3. Support agents became specialists
With 90% of routine queries automated, their two support agents evolved into product specialists. They now conduct onboarding calls, write advanced guides, and maintain the knowledge base — work that directly improves retention rather than just fighting fires.
What the Support Lead Says
"Honestly, I was skeptical. I thought the AI would give wrong answers and create more work — frustrated users escalating because the bot messed up. The opposite happened. The answers were better than what we had in our help center because the AI could synthesize across multiple articles and give a direct answer instead of linking to a wall of text."
— Kartik, Support Lead at ProjectFlow
The 10% That Still Goes to Humans
The 10% of tickets that reach human agents falls into predictable categories:
These are the tickets that actually benefit from a thoughtful human response. The agents aren't burned out on "how do I share a project?" anymore — they have mental bandwidth to handle edge cases well.
How to Replicate This for Your SaaS
If you run a SaaS product and your support team is answering the same questions repeatedly:
1. Audit your top 20 ticket types — What percentage are tier-1 (documented answers)?
2. Export your help center — Callsup can index it in under an hour
3. Deploy the widget in-app — Not just on the marketing site; put it where users are when they hit a problem
4. Set smart escalation — Let the AI handle 80%+ but make human access instant and graceful
Most SaaS products have a 70–85% tier-1 ticket rate. If that's true for you, Callsup can deflect most of that within 30 days.
*See how Callsup would work for your SaaS — start a free trial and have it live today.*