Parloa builds service agents customers want to talk to

In Parloa’s early days, Co-founder Stefan Ostwald spent a day inside an insurance call center, where his team had been building early voice experiences. Sitting alongside agents, he listened to the same conversations play out again and again: password resets, policy questions, routine changes. He realized much of that work could be automated.

After that experience, Berlin-based Parloa ⁠ (opens in a new window) began building rule-based voice agents to automate high-volume customer interactions.

With the emergence of ChatGPT, the company evolved to build what is now its AI Agent Management Platform (AMP), built on a new generation of models including GPT‑5.4.

Voice introduces a different set of constraints than text-based chat. Every interaction runs through a low-latency pipeline: speech-to-text, model reasoning, and text-to-speech.

This pipeline makes latency critical. Even small delays in the model layer compound into noticeable pauses for the caller, shaping how models are selected and optimized.

“When a new model comes out, we run our benchmarking suite against it,” says Matthäus Deutsch, Senior Applied Scientist. “It’s very important for us that things do not only work in theoretical benchmarks but in actual real use cases.”

Parloa works closely with OpenAI to optimize performance for real-time use cases, focusing on latency, response quality, and instruction following. The team continuously evaluates and stress-tests new model iterations in production-like environments before rolling them out to live customer interactions.

Today, Parloa’s agents handle millions of conversations across industries including retail, travel, and insurance, supporting use cases that range from support automation to revenue-generating flows such as teleshopping.

AMP gives enterprises a way to design, deploy, and manage customer service interactions at scale. Instead of mapping out rigid intents and flows, teams define behavior in natural language, connect to internal systems, and iterate quickly using built-in simulations and evaluations.

Parloa runs these interactions end to end, handling everything from simple routing to complex, multi-step requests. The focus is on consistency in production, where performance, latency, and edge cases all matter. To get there, Parloa continuously tests models against real customer scenarios before deploying them.

Parloa’s Agent Management Platform (AMP) is designed for business users and subject matter experts to be able to build AI agents without writing code.