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Hamming AI Partners with Cisco to Enhance Voice Agent Reliability
Hamming AI announces strategic partnership with Cisco to help their customers build and maintain reliable AI voice agents through automated testing and production monitoring.
Recent posts
CX Lessons from 35 Years in Contact Centers
Extracted from our podcast The Voice Loop, this episode recap features Brian Jeppesen sharing hard-won lessons from 35+ years in contact centers. Learn why customer effort beats NPS, how to QA your virtual assistant, and why the companies that deploy voice AI to reduce friction (not cut costs) see 390% ROI.Sumanyu SharmaWhy Evaluations Alone Won't Save Your Voice Agent
LLM evaluation tools score what happened. Voice agents need to test what will happen. Learn why simulation + evaluation platforms are essential for voice, and when evaluation-only tools are enough.Sumanyu SharmaBuilding Enterprise Voice Agents in Europe
Extracted from our podcast The Voice Loop, this episode recap explores how Versaia is building enterprise-grade voice agents in the Netherlands. Learn about the unique challenges of Dutch language support, earning enterprise trust after chatbot failures, and why Soniox became their secret weapon for multilingual STT.Sumanyu SharmaVoice Agent Drift Detection: Monitor Model and Behavior Changes
How to detect voice agent drift across STT, LLM, and TTS layers. Covers baseline establishment, continuous monitoring, and alerting on quality degradation.Sumanyu SharmaHow Hamming Became the First to Market in AI Voice-Agent QA
Hamming was first to market in dedicated AI voice-agent QA: audio-native testing and monitoring over real phone calls. We publicly launched August 15, 2024, got TechCrunch coverage in September, and raised our seed round in December. A competitor launched on Product Hunt on December 4, 2024. Many others entered in 2025 and beyond. With 10M+ mins protected, we have one of the longest track records and among the largest datasets in voice agent QA.Sumanyu SharmaBuilding GenAI Voice Agents: A Complete Evaluation Guide
A comprehensive framework for evaluating voice agent performance. Learn why voice evaluation differs from text-based testing, how architectural choices affect observability, key metrics that matter, and how to implement automated evaluation at scale with human-in-the-loop feedback cycles.Rahul ParundekarTesting Multi-Modal AI Agents: Voice, Chat, SMS, and Email
How to test AI agents that handle voice, chat, SMS, and email interactions. Unified evaluation framework for consistent quality across modalities, based on 10M+ mins protected.Sumanyu SharmaTop Voice Agent Testing Platforms 2025: Complete Comparison Guide
Compare the top voice agent testing platforms in 2025. Detailed feature matrix, pricing, and recommendations based on Hamming's 10M+ mins protected across 10K+ voice agents.Sumanyu SharmaLogging & Analytics Architecture for Voice Agents
Explore how logging, storage, and retention shape the reliability and compliance of voice agents, and understand the architectural choices behind routing, observability, and regulatory alignment.Sumanyu SharmaTesting Voice Agents for Financial Services
Financial voice agents sit on regulated data and core banking workflows. Learn why authentication, PCI handling, tool calls, and model drift need to be validated under real conditions before deployment.Sumanyu SharmaTesting Voice Agents for Healthcare
Learn about automated testing for healthcare voice agents: validate safety, HIPAA alignment, clinical accuracy, escalation triggers, and peak-load performance with Hamming.Sumanyu SharmaAre Speech-to-Speech Models Ready to Replace Cascade Models?
Extracted from our podcast The Voice Loop, this episode recap explores why Ultravox moved from cascaded pipelines to speech-to-speech models. Learn how GLM 4.6, audio-native training, and Voice Agent Bench shift the trade-offs in latency, reasoning, and real-world performance.Sumanyu SharmaHow to Test Voice Agents Built with LiveKit
Explore how to test voice agents built with LiveKit.Sumanyu SharmaHow to Test Voice Agents Built with Vapi
Explore three ways to test voice agents built with Vapi.Sumanyu SharmaHow to Test Voice Agents Built with Retell
Explore three ways to test voice agents built with Retell.Sumanyu Sharma7 Voice Agent ASR Failure Modes in Production
Explore seven real-world ASR failure modes teams see in production voice agents, plus how to test and monitor for these failure modes to build and deploy reliable voice agents.Sumanyu SharmaThe Ultimate Guide to ASR, STT & TTS for Voice Agents
A practical guide to ASR/STT and TTS in voice agents, covering accuracy, latency, prosody, testing, and production monitoring.Sumanyu SharmaHIPAA-Compliant Voice Agents: How to Build and Test Safely
Learn how Hamming helps healthcare teams ensure HIPAA-compliant voice agents through scenario-based testing, compliance validation, and continuous QA.Sumanyu SharmaHamming Is Now SOC 2 Compliant
We're proud to announce that Hamming has successfully completed our SOC 2 audit and is now SOC 2 compliant.Sumanyu SharmaWhy Voice AI Still Breaks at Scale
Extracted from our podcast The Voice Loop, this episode recap explores why voice agents continue to break at scale despite advances in STT, TTS, and LLMs. Learn how organizational processes, vendor chains, and operational infrastructure shape voice agent reliabilitySumanyu SharmaYour Audio Quality Might Be Breaking Your Voice Agents
Extracted from our podcast The Voice Loop, this episode recap explores how input audio quality is one of the biggest bottlenecks in voice AI. Learn how degradation across devices, environments, and transmission layers impacts ASR accuracy.Sumanyu Sharma5 Failure Modes That Make Voice Agents Unsafe in Clinical Settings
Explore the five failure modes that undermine clinical voice agents. Learn how perception stability, guardrail enforcement, multi-agent reasoning, workflow sequencing, and latency shape real clinical reliability, and why testing the full pipeline is essential for safe healthcare deployments.Sumanyu SharmaEvaluating Conversational AI: Why Accuracy Isn't Enough
Explore why accuracy is only one dimension of conversational AI performance. Learn how audio stability, ASR behavior, retrieval alignment, and multi-turn reasoning shape real reliability and why evaluating the full pipeline is essential for production-ready voice agents.Sumanyu SharmaBeyond Benchmarks: What's Next for Voice Agent QA in 2025
Extracted from our podcast The Voice Loop, this episode recap explores how Voice AI teams are moving beyond synthetic benchmarks to real reliability, covering demos, ASR bottlenecks, and the future of production-ready voice QA.Sumanyu SharmaHow to Use AI Voice Agent Analytics to Improve CSAT
Learn how to use AI voice agent analytics to improve CSAT. Discover how Hamming's observability platform helps enterprises measure latency, model accuracy, and user reactions to turn customer satisfaction into a real-time operational signal.Sumanyu SharmaBest Practices to Improve AI Voice Agent Reliability
Voice agent reliability is the defining measure of quality for production voice AI. Learn best practices including instrumentation, version control, regression testing, failure handling, and continuous monitoring to build reliable voice agents.Sumanyu SharmaAI Voice Agent Regression Testing
Learn how to implement effective regression testing for AI voice agents using Hamming's Regression Detection Framework. Prevent performance degradation and ensure consistent quality across updates with automated behavioral drift detection.Sumanyu SharmaVoice User Experience
Learn how to design and optimize voice user experience (VUX) for AI voice agents. Discover best practices for creating intuitive, engaging, and effective voice interactions.Sumanyu SharmaVoice Agent Performance
Learn how to measure and improve voice agent performance with key metrics like latency, accuracy, prompt adherence, escalation rates, and goal completion. Discover common challenges and how Hamming provides the right infrastructure with testing, monitoring, dashboards, and feedback loops.Sumanyu SharmaAn Introduction to Voice Agent Guardrails
Learn how voice agent guardrails protect your AI systems from risks like prompt injections and compliance violations. Discover how testing and monitoring ensure production-ready voice agents.Sumanyu SharmaHow to Write Voice Agent Prompts That Don't Break in Production
Most prompts fail once they hit production. Learn how to design modular, testable, and reliable prompts that don't break in production.Sumanyu SharmaAI Voice Agent Compliance & Security
Voice AI poses serious compliance risks, from HIPAA violations to PCI DSS failures. Discover how to keep voice AI agents secure and compliant. Test against key regulatory framework including HIPAA and PCI DSS risks and monitor every call in real time.Sumanyu SharmaTesting Multi-Step Voice Agents: Why End-to-End Observability Changes Everything
Multi-step voice agents introduce cascading failures, context loss, and latency challenges. Learn why traditional testing falls short and how end-to-end voice observability ensures reliable production calls.Sumanyu SharmaWe Jailbroke Grok's AI Companion: Ani
We jailbroke Grok’s AI companion Ani using prompt injection testing, exposing latency, QA, and guardrail failures. Here’s why observability matters.Sumanyu SharmaHow to Optimize Latency in Voice Agents
Optimizing latency is challenging if you don't actively monitor it. Learn how to track the key latency metrics in order to optimize latency effectively.Sumanyu SharmaHow to Evaluate AI Voice Agents Performance in Production with Hamming
Learn how to evaluate AI voice agent performance in production with Hamming. Track accuracy, latency, reliability, and end-to-end observability to ensure agents deliver consistent and reliable conversations.Sumanyu SharmaVoice Observability: The Missing Discipline in Conversational AI
Voice observability is the missing discipline in Conversational AI. Learn what it means to define voice observability, what to measure and how to measure each layer of the stack.Sumanyu SharmaWhy State Transitions Matter in Multi-Agent Voice Systems
State transitions are the most critical failure points in multi-agent voice systems. This guide covers the mechanics of handoffs between agents, common breaking points, and practical strategies for designing, testing, and monitoring reliable transitions.Sumanyu SharmaAn Intro to Production Monitoring for Voice Agents
Proactively monitor your AI voice agents in production with Hamming's always-on Heartbeats. Catch silent failures, reduce QA overhead, and get alerted instantly when critical conversation paths break.Sumanyu SharmaThe 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.Sumanyu SharmaVoice 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.Sumanyu SharmaHamming 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.Sumanyu SharmaWhy the Best Voice AI Teams Choose Hamming
The best voice AI teams, from healthcare systems to global enterprises, choose Hamming for testing before launch, monitoring after, and one loop between them. Here is what they look for.Sumanyu SharmaHamming 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.Sumanyu SharmaSelective 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.Sumanyu SharmaEnhanced 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.Sumanyu SharmaHamming AI Partners with Fluents.ai for Enhanced Voice AI Testing
Fluents.ai customers can connect Hamming AI's comprehensive voice agent testing suite to enterprise-grade AI Voice Agents workflows.Sumanyu SharmaHamming 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.Sumanyu SharmaHamming 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.Sumanyu SharmaThanksgiving Update from Hamming AI
A brief note of thanks from our team as we continue building better voice AI testing solutions.Sumanyu SharmaAI 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.Sumanyu SharmaHamming 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.Sumanyu SharmaCan 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.Hokyung (Andy) Lee