Customer case study
Rho reviews every customer interaction, catches 4x more issues, and cuts escalations by 3x.
Rho’s manual QA process could inspect only 3–5% of customer interactions and often found issues after they had already escalated. With Rulebase, Rho reviews 100% of phone, email, and chat conversations, catches four times more issues, cuts escalations by three times, and spends about 90% less time on manual review.
Rho at a glance
- Customer
- Rho — all-in-one business banking platform
- Industry
- Financial services / business banking
- Channels
- Phone, email, and chat
- Rulebase used for
- 100% interaction review, real-time alerts, QA workflows, and coaching
The challenge: QA was always looking backwards
Rho had quality assurance in place from day one. But manually pulling a small sample of calls, emails, and chats meant the team could see only a fraction of the customer experience. Finding and assembling the right conversations was slow, and a problem might recur before the review reached it.
“Customer experience is a top priority at Rho, and quality assurance is something we implemented from day one. But we always felt we were behind, catching up on issues a little too late. With Rulebase, we can be forward-looking.”
Stas Johnson-ChyzhykovChief Operating Officer, Rho
From a 3–5% sample to every interaction
Rulebase reviews Rho’s phone, email, and chat conversations continuously against its own operating standards. Instead of waiting for a weekly sample, managers see where a conversation went off track, which issue is repeating, and what needs attention now.
That wider coverage is not just more data. It gives Rho enough evidence to distinguish an isolated miss from a systemic gap, then direct coaching or an operational fix to the right place.
“Rulebase enables us to catch what sampling never could. What would have taken a week to pull samples together, Rulebase does in minutes. That’s allowed us to go deeper in our reviews, be less selective, and reduce customer escalations by 3x while we do it.”
Jeff PasquerellaHead of Compliance, Rho
Results
| Measure | Before | With Rulebase |
|---|---|---|
| QA coverage | 3–5% manual sample | 100% of interactions |
| Issues caught | Limited by the sample | 4x more issues surfaced |
| Customer escalations | Problems often found after the fact | Reduced by 3x |
| Manual review | A week to assemble some samples | Reduced by about 90% |
| Channels | Calls, emails, and chats reviewed selectively | Phone, email, and chat reviewed continuously |
One improvement loop for human and AI agents
The same review layer can evaluate conversations handled by people or AI. It identifies what prevented a correct, low-effort resolution, then turns that finding into the appropriate next action: a coaching draft for a human agent, or a prompt, knowledge, tool, or workflow fix for an AI agent.
For Rho, this makes QA an operating system for improvement rather than an archive of scores. The team can find more issues without adding reviewers and act before those issues become escalations.
Frequently asked questions
How much of its customer support does Rho review with Rulebase?
Rho reviews 100% of customer interactions across phone, email, and chat with Rulebase. Before Rulebase, the team manually sampled about 3–5% of interactions.
What measurable results did Rho achieve with Rulebase?
Rho caught four times more issues, cut customer escalations by three times, and reduced manual review by roughly 90%, without adding headcount.
How did Rulebase change Rho’s QA process?
Rulebase changed QA from a selective, after-the-fact audit into continuous review. Managers can see when a conversation goes off track and act on the finding while it is still useful.
Does Rulebase review human and AI agents?
Yes. Rulebase reviews customer conversations handled by both human and AI agents, scores the interaction against the company’s standards, and turns findings into fixes such as coaching, prompt changes, and knowledge updates.
See what Rulebase would find in your customer conversations
Review every interaction across your human and AI agents, identify what is preventing resolution, and put the highest-impact fixes first. .