Hamming AI Blog

Guide to Choosing the Right Voice Agent Stack

A comprehensive framework for selecting and integrating voice agent components for production, covering architecture patterns, platform comparisons, and a 4-layer quality framework with a 30-day implementation plan.

The Anatomy of a Perfect Voice Agent Analytics Dashboard

Discover how to build analytics dashboards designed for AI voice agents, with insights into KPIs that matter, real-time monitoring, and avoiding common pitfalls when measuring LLM performance.

A Guide to Quality Assurance for AI Voice Agents

Learn how to implement AI voice agent quality assurance with proven strategies for voice agent testing. Discover the 4-layer framework for ensuring AI voice agent QA across infrastructure, execution, user satisfaction, and business outcomes.

Voice Agent Analytics: Why Legacy Analytics Solutions Don't Work Anymore

Legacy tools fail at understanding AI voice agents. Learn how modern analytics offer real-time insights, improve accuracy, and surface blind spots.

Hamming vs. Retell & Vapi QA Testing: Why platform QA isn't enough

Scripted voice tests pass in the lab but fail in production. Compare Hamming's stress-testing and live observability with Vapi and Retell's happy-path suites, then learn how to harden your agent.

Hamming AI Raises $3.8M Seed Round

We're excited to announce our $3.8M seed round led by Mischief, with participation from YCombinator, AI Grant, Pioneer, Coalition Operators, Coughdrop, and notable angels.

DTMF Support for Comprehensive Voice Agent Testing

Automate DTMF testing with Hamming AI's new feature. Simulate keypad inputs, test menu navigation, and validate voice agent responses to DTMF tones in your automated test scenarios.

Selective Scenario Re-runs for Voice AI Testing

New feature enables teams to re-run selected scenarios from existing datasets, streamlining the testing process for voice AI agents with targeted testing.

Enhanced Call Debugging with SIP Status Tracking

New debugging features provide clear insights into call termination and SIP status, helping teams quickly identify and resolve voice AI agent issues.

Hamming AI Partners with Fluents.ai for Enhanced Voice AI Testing

Fluents.ai customers now get access to Hamming AI's comprehensive voice agent testing suite, while Hamming AI customers receive 15% off Fluents.ai's enterprise-grade AI Voice Agents workflows.

Hamming AI Launches Advanced Call Analytics for Voice Agent Testing

New analytics module provides comprehensive performance visualization for AI voice agent testing, including latency metrics, call durations, and LLM-based evaluations.

Hamming AI Integrates with Hume AI for Enhanced Voice Agent Monitoring

Access real-time emotional characteristics monitoring in production calls with Hume AI integration. Monitor pitch, tone, and rhythm to gauge caller sentiments during voice agent interactions.

Thanksgiving Update from Hamming AI

A brief note of thanks from our team as we continue building better voice AI testing solutions.

Multi-Language Support for Voice AI Testing

Test your AI Voice Agents in 11 languages including Dutch, English, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish. Ensure reliable voice interactions across global markets.

AI Grant Demo Day: A Weekend of Learning and Connection

Reflecting on an energizing weekend at AI Grant Demo Day and doubling down on our mission to make voice AI testing more robust and accessible.

Customer Spotlight: How Lilac Labs (YC S24) Ensures Drive-Thru Order Accuracy with Hamming AI

Learn how Lilac Labs automates drive-thru order testing to ensure accuracy and handle complex scenarios like dietary restrictions and allergies.

Hamming AI Partners with Retell AI for Enhanced Voice Agent Testing

Hamming AI and Retell AI announce strategic partnership to provide real-time monitoring and automated testing for AI voice agents, offering immediate alerts for agent mistakes and hallucinations.

Can LLMs find bugs in large codebases?

We bet your LLM can find a bug in a snippet of code. But how about 25 pages of code? We propose a new 'needle in a haystack' analysis called 'Bug in the Code Stack' that tests how well LLMs can find bugs in large codebases.