Research that advances artificial intelligence for everyone
AI still leaves too many questions unanswered. At Parloa Labs, we open the black box to understand what today's systems are capable of and where they can go next.
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Our research areas
We study the frontiers of voice and agentic AI and publish our work openly, share practical learnings, and give back to the broader community’s understanding of what makes conversations work.
Voice infrastructure
Building telephony systems that handle real-time voice interactions, e.g., speech recognition, synthesis, latency optimization, and audio quality at scale.
Agent architecture
Designing AI agents that reason, plan, and execute complex tasks: model orchestration, multi-agent systems, and prompt engineering approaches.
Agent capabilties
Developing reusable skills for common scenarios, such as routing, authentication, knowledge retrieval, and integrations with backend systems.
Observability & optimization
Understanding agent behavior through analytics, simulations, evaluations, and continuous improvement based on production data.
Latest findings and discoveries
Beyond go-live: How live conversations make great AI even better
Go-live isn't the finish line for voice AI. Learn how Parloa turns production conversations into evidence for continuous agent improvement.
Who improves the agent that improves the agent? Harness engineering at Parloa
Inside Conversation Defense: How Parloa’s guard model reads the whole conversation
Read how and why Parloa engineered a third security layer, Conversation Defense, to work with Azure Security and Content Filters, for increased agentic security.
The most expensive voice AI mistakes happen before you start building
Parloa's agent architect explains why the most expensive voice AI mistakes happen during discovery, long before design even begins.
The stateless MCP standard for voice AI
Why delegation matters more than ever in enterprise engineering
As AI agents take over execution, human engineers are shifting their focus to managing agents like managers manage people. Here's what that means in practice.
Your agent is more than its model: Why Parloa engineers have doubled down on the agent harness
An agent's model is only sometimes the problem. Ready how the agent harness can be the real determining factor of if enterprise performance is won or lost.
Building AI agents on shifting ground
LLM end-of-life cycles are measured in months, not years. Here's how Parloa adapts fast and improves their agent performance with each evolution.
How we built Parloa's model evaluation system
Parloa's AMP Dojo System allows scalable and reproducible experimentation and testing of different components of Parloa’s product.
We believe the best innovation happens at the intersection of theory and practice
Every day, Parloa’s AI agents handle millions of interactions across industries and languages. This gives us a unique vantage point to identify real challenges, test solutions at scale, and contribute meaningful findings back to the research community.