Tools and stack decisions
Voice agent platforms compared
Practical comparison for enterprise use cases
Little Brother Labs
Context
Choosing a voice AI platform is complex. Marketing claims don't match production realities.
Enterprise buyers need to evaluate latency, integration, compliance, cost and vendor lock-in.
The right choice depends on use case, existing stack and risk tolerance.
What changed with AI systems
Voice AI platforms have matured from demos to production. Latency is now under 2 seconds for most platforms.
Integration is the real challenge: connecting to telephony, CRM, calendars and compliance systems.
The shift is from choosing the best model to choosing the best end-to-end system for your context.
How to approach this in your organisation
- →Define your use case precisely: inbound only? Outbound? After-hours? What languages and accents?
- →Test latency in your environment: don't trust benchmarks, measure with your telephony and prompts.
- →Evaluate integration complexity: what APIs exist, what custom work is needed, what's the ongoing maintenance?
- →Review compliance features: call recording, data residency, consent management for your jurisdiction.
- →Calculate total cost: licensing, usage fees, integration, ongoing tuning and support.
Metrics and risks
Key metrics
- →Latency: median and p95 response time from caller input to agent speech.
- →Accuracy: word error rate and intent recognition accuracy in production.
- →Uptime: platform availability excluding upstream telephony issues.
- →Integration time: days to connect to core systems and deploy pilot.
Risks to consider
- →Vendor lock-in: difficulty switching platforms if needs change or pricing increases.
- →Latency spikes: inconsistent performance that frustrates callers.
- →Integration failures: broken connections to CRM or calendar during peak volume.
- →Compliance gaps: missing features for recording, consent or data residency requirements.
Want to discuss this topic?
Talk to us about implementing these approaches in your organisation.