5,000 Conversations a Day.
Without Hiring.
Support volume grows with your revenue; headcount shouldn't have to. We build AI agents that handle the routine tickets, route the hard ones to the right person, and keep response times under 2 minutes.
routine repeatable questions
(Zendesk / Gartner research)
on our SaaS project
SaaS support project
Support That Scales Linearly With ARR
The growth problem every SaaS company eventually hits: more customers means more tickets means more agents. The economics of that are bad.
Double your revenue and support volume doubles too, so you need twice the agents. The margin advantage of software disappears into a team that grows with every new account.
Password resets, billing questions, "how do I export data" — agents answer these dozens of times a day. The work drains people, and the turnover that follows is expensive and predictable.
Ship a feature or hit an incident and tickets spike — but the team was staffed for average load. The breaches that follow are when customers decide you're not enterprise-ready.
Five years of docs, help articles, and runbooks. Agents spend three minutes finding the right one; customers give up and open a ticket. The knowledge exists — it just can't be found fast.
How the Support System Works
5,000 Support Conversations a Day — 78% Without Human Involvement
A B2B SaaS platform at 5,000+ daily conversations, an 8-hour first response time, and support costs rising with revenue. We built a support system that answers from their full knowledge base, resolves routine tickets on its own, and routes complex ones to the right person with context attached.
5 support agents whose routine ticket load (78% of volume) was automated (€38K × 5 × 78% reload factor = est. €148K direct) + reduced churn contribution from faster SLAs (est. €62K). Payback: ~7 weeks.
See this and 8 more case studies →A Support System That Scales Independently of Headcount
The goal is to make the common case instant and the complex case better — not just to deflect tickets.
Answers grounded in your own docs, help articles, and runbooks. The agent gives a specific answer, not a link to go search, and stays current as your docs change.
Every ticket is sorted by type, priority, and skill needed before it hits a queue. Routine ones resolve automatically; the rest reach the right person with a draft reply attached.
The agent absorbs volume spikes without missing your SLAs. A 10x surge after an incident doesn't breach enterprise commitments. Your people handle the incident, not the ticket storm behind it.
When the agent isn't sure, it hands off — with its reasoning, the conversation, and the sources attached. Your team doesn't start from scratch; they review what the AI prepared.
The Tools Behind It
What does your monthly ticket volume look like?
Tell us your volume, your team size, and your most common ticket types. We'll show you what's automatable and model the ROI against your support cost.
Size up my support volume