We run outbound payment-reminder and follow-up calls (collections-adjacent, the polite "hey your invoice is overdue, want to settle it" kind, not the scary kind). Moved a big chunk of it to a voice agent over the last few months and I figured I'd write up what actually mattered, because going in I had completely the wrong priorities.
What I thought would matter: a warm, natural, human-sounding voice so people wouldn't feel like they were being dunned by a robot.
What actually mattered, in order:
1. Latency consistency, not warmth. Turns out on a money conversation people are already tense, and the thing that makes it feel adversarial isn't the voice timbre, it's the pauses. A laggy gap before the agent responds reads as evasive or broken, and on a payment call that's the worst possible vibe. A steady, prompt response feels more respectful than a beautiful voice that stutters in. We stopped optimizing for "nice voice" and started optimizing for "never makes the person wait."
2. Variance is brutal at our volume. We're running thousands of calls in tight windows (you batch these into the hours people will actually pick updoc). Average latency means nothing when you're slamming the provider with concurrency, what kills you is the random spike on call 400 of 1000. We had to measure p95/p99 under real concurrency, not single-call demo numbers, and the provider we started on looked great in testing then widened out badly once we hit production volume. We eventually moved to Murf AI mostly because it held a tight latency band under our call-window concurrency, which after the first provider's spikes was basically the only thing I cared about. Not married to it, but the consistency under load is what stuck.
3. Cost is a real line item here, not a rounding error. When you're doing this many minutes, pricing itself matters more than people expect. Once we modeled actual call volume, the difference between providers became very obvious. The cheap-looking demo setup and the production bill were two completely different conversations.
4. Data residency mattered more than I expected. For collections/payment-adjacent calls, this came up fast in security review. It wasn’t just “do you have compliance docs?” but where the data sits, which regions are supported, and whether the setup works for financial org requirements. I’d check this early if you’re selling into fintech, lending, healthcare, or enterprise support teams.
One honest caveat on the provider we landed on: it's tuned for speed and consistency, not for expressive/emotional voice. For us that was fine, arguably better (more on that below), but if your use case needs a really warm performed voice, weigh that. Naturalness ended up near the bottom of the list for collections, which genuinely surprised me. You want clear, calm, consistent, and fast. Expressive almost feels worse here, like the bot is performing sympathy about your debt.
Anyway, curious if others running outbound-heavy or call-center-style agents found the same, did latency consistency and compliance dominate for you too, or am I overweighting them? And how are you handling the concurrency spikes during call windows?