5,000 Conversations a Day.
Without Hiring.
Support volume scales with your ARR. Headcount shouldn't have to. When 60–80% of tickets are the same repeatable questions — password resets, billing queries, how-to requests — answering them manually is a choice, not a requirement. We build the AI agents that handle your routine volume, route the complex conversations to the right human, and keep response times under 2 minutes regardless of queue length.
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 relationship are bad.
You double ARR. Support volume doubles. You need twice the agents. The cost of customer success is now a growth tax. The margin improvement from scaling a software product disappears into a support team that grows with every new account.
Password resets, billing questions, "how do I export data", "why is the sync broken" — agents answer these dozens of times a day. The work is draining, not engaging. Turnover in a support team that handles high-repetition tickets is expensive and predictable.
When you ship a feature, run a campaign, or have an incident, tickets spike. The team was staffed for average load. Average load isn't what happens. SLA breaches during peak moments are when customers decide your product isn't enterprise-ready.
5 years of documentation, help articles, release notes, internal runbooks. Agents search for 3 minutes to find the right article. Customers search for 3 minutes and open a ticket instead. The knowledge exists — it just can't be found at speed.
What We Actually Built
5,000 Support Conversations a Day — 78% Without Human Involvement
A B2B SaaS platform at 5,000+ daily support conversations, an 8-hour average first response time, and a support cost growing in line with ARR. Every new enterprise account added ticket volume that needed more agents to handle. We built a multi-agent support system — a retrieval-augmented chatbot with access to their full knowledge base, an automated resolution layer for routine ticket types, and an intelligent escalation path that assigns complex conversations to the right team member 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 full case study library →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.
Your documentation, help articles, release notes, and internal runbooks indexed and queryable in natural language. The agent retrieves the relevant content and generates a specific answer — not a link to search. Updated automatically as your docs change.
Incoming tickets classified by type, priority, and required expertise before they reach a queue. Routine tickets resolve automatically. Complex tickets route to the right team member with context and suggested response already attached.
The agent handles volume spikes without SLA degradation. A post-incident spike of 10x normal volume doesn't breach your enterprise commitments. The humans deal with the incidents — not the ticket storm that follows.
When the agent's confidence is below threshold, it escalates — with its reasoning, the conversation history, and the retrieved context attached. Agents don't start from scratch. They review and extend what the AI already prepared.
What We Build On
What does your monthly ticket volume look like?
Tell us your volume, your current team size, and your most common ticket categories. We'll show you what's automatable and model the ROI against your specific support cost.
Get a scoped estimate