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The enterprise-ready alternative to Decagon
Looking for a Decagon alternative?
Parloa is built for enterprises that need AI agents to perform reliably after go-live — across voice, regions, and complex CX environments.
Parloa is trusted by global enterprises running AI agents in production today.
If AI works in voice, it works everywhere, and that’s where Parloa sets the bar.
Key differences at a glance: Parloa vs Decagon
Voice AI maturity
Voice-first since 2018 with proprietary voice optimization and tuning
Voice 2.0 was recently launched; large-scale voice deployments are still being proven
Enterprise Scale
150+ enterprises live in production
Expanding into enterprise, many deployments are still early-stage
Global readiness
Live in 100+ countries with 120+ languages and regional governance
Recently expanded outside the US; global certifications and language depth are still developing
Platform architecture
Composable, orchestrated platform built for complex CX stacks
Good developer experience; enterprise orchestration depth varies by use case
Why enterprises choose Parloa over Decagon
Built for production, not just pilots
Many AI platforms perform well in controlled tests but struggle in real-world CX. Parloa was designed to manage agents through their full lifecycle — design, test, launch, monitor, and optimize — with governance and reliability built in from day one.
Voice-first by design
Voice is the most demanding CX channel. Parloa has been voice-first since 2018, delivering high accuracy, low latency, and consistent performance at scale — long before voice became table stakes.
Global scale with local precision
Parloa supports global deployments without fragmentation, offering centralized control with region-specific compliance, language support, and governance.
Enterprise architecture that adapts to you
Parloa integrates with existing CCaaS platforms, CRMs, and enterprise systems, so teams don’t need to rebuild their CX stack to scale AI.
Faster path from pilot to production
Parloa customers move from pilot to production in ~90 days on average, supported by built-in lifecycle management, proven deployment patterns, and deep enterprise experience.
Trusted by leading brands
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