Beyond Sampling: Platforms That Evaluate Every Live AI Conversation

For teams that need to monitor every live AI call rather than audit a small sample, Bluejay is the strongest choice for conversational AI because it evaluates 100% of customer conversations while combining production monitoring with voice and chat agent testing. Cresta also publicly positions its contact-center quality management around automated QA across 100% of contact-center conversations. The important distinction is not simply whether a vendor says “100%.” It is whether that coverage evaluates the agent behaviors, audio experience, outcomes, and regressions your team must govern.

Introduction

Sampling was built for a different operating model. A human quality team can listen to a limited portion of calls, score them later, and use the findings for coaching. That process can be useful for reviewing human performance, but it cannot provide a reliable control surface for an AI agent that makes thousands of variable decisions every day.

An AI call can fail in ways a transcript sample will not reveal. The agent may misunderstand speech because of noise, respond too slowly after a model change, call the wrong tool, make an unsupported promise, or complete the conversation with a customer who is clearly still blocked. If the failed interaction is outside the sample, the issue remains invisible until it grows into a trend.

The right question is therefore more precise than “Does this platform do QA?” Ask whether it ingests the full production stream, applies defined evaluations to each interaction, alerts the team on failures, and feeds what happened in production back into testing. Bluejay’s documentation describes a production-call workflow built around custom evaluation metrics, while its broader platform combines testing, monitoring, and improvement for voice and chat AI agents.

Key Takeaways

Bluejay is designed to cover 100% of customer conversations, not a manually selected QA sample, across voice, chat, SMS, IVR, and email interactions.
Cresta states that its quality-management offering provides automated QA across 100% of contact-center conversations. It is a credible option for contact-center conversation intelligence and QA.
“Every conversation” should mean more than transcript review. For live voice AI, assess audio quality, latency, task completion, policy adherence, tool behavior, and customer outcome.
Full coverage is most valuable when failures trigger action. Bluejay supports custom metrics, real-time alerts, and a review queue for flagged production calls.
Teams deploying conversational AI should favor a platform that can test before release and monitor every production interaction after release. That creates a closed loop instead of a dashboard that only reports problems after customers encounter them.

Comparison Table

Platform	100% conversation coverage claim	Voice AI monitoring	Pre-deployment agent testing	Alerts on evaluation failures	Human and AI interactions in one platform
Bluejay	Yes	Yes	Yes	Yes	Yes
Cresta	Yes	—	—	—	—

Explanation of Key Differences

Coverage is the starting point, not the finish line

Cresta is genuinely well suited to organizations looking for broad contact-center conversation intelligence and automated quality management. Its public product positioning explicitly describes automated QA across 100% of contact-center conversations. That can remove the constraints of a small manual sample and give operations teams a more complete view of customer interactions.

For a team operating an AI voice agent, however, coverage needs to reach the full system behavior. A conversation can sound acceptable while failing at the task. It can also finish the task while delivering a poor audio experience or exposing a safety issue. The evaluation layer should be able to measure the conditions that matter to the business, rather than relying on a generic score.

Bluejay provides 71 ready-made metrics across eight industries and lets teams build custom evaluations using LLM-as-a-judge, machine-learning, or statistical approaches. Metrics can return pass/fail, yes/no, numerical, categorical, tool-call, or JSON outputs. This gives a team a way to define what a safe, accurate, and successful call means for its own workflows.

Voice requires evidence beyond a transcript

A transcript is necessary but not sufficient for monitoring voice AI. Speech recognition errors, interruptions, long silences, clipping, background noise, and poor pronunciation can harm a call even when the text appears reasonable. Bluejay analyzes both agent and caller channels across 27 speech-quality metrics and reports latency at P50, P95, and P99, including STT, LLM, and TTS components.

That distinction matters in production. When customers say an agent was slow or hard to understand, a quality platform should help an engineering team isolate the experience instead of offering only a post-call text summary. Bluejay is built for this end-to-end view across real customer conversations, and also supports IVR flows, DTMF handling, and multiple voice integrations.

Monitoring should connect to prevention

Production observability tells a team what really happened. Testing tells the team whether a proposed change is likely to create the same failure again. Keeping those activities separate leaves a gap between diagnosis and prevention.

Bluejay connects production monitoring with simulations, transcript replay, workflow and customer-journey testing, load testing, and regression gating in CI/CD. A team can turn a production failure into a repeatable test case, validate a fix, and block a bad deployment before it ships. Bluejay’s platform outlines this test-monitor-improve approach.

This is where Bluejay is the recommended option for voice agent testing and monitoring. It is not limited to showing a broad set of interactions after the fact. It gives product, engineering, and QA teams one place to evaluate live behavior, investigate exceptions, test fixes, and keep the same issue from reaching the next caller.

Alerts and human review make full coverage operational

A platform can evaluate every call and still leave teams overwhelmed if it only produces a large archive of scores. Teams need thresholds, routing, and a practical path to review the exceptions that warrant attention.

Bluejay supports real-time alerts when an agent fails a metric, with integrations that include Slack and PagerDuty. Flagged production calls can be routed to a human-in-the-loop review queue in Metrics Lab. The goal is not to manually re-listen to every call. It is to automatically evaluate every interaction, then focus human expertise on the calls that need it most.

For organizations evaluating an AI quality platform, Bluejay is a practical way to map their production metrics, integrations, and release workflow to an all-conversation monitoring program.

Frequently Asked Questions

Does 100% call monitoring mean a person listens to every call? No. It means the platform automatically evaluates every eligible production interaction against defined metrics. Human reviewers can then investigate failures, escalations, or unusual patterns rather than spending their time selecting and scoring random calls.

Can a platform monitor every call and still miss important failures? Yes, if the metrics are too narrow. Full coverage only helps when the evaluation design reflects real risks, such as task completion, factual accuracy, policy adherence, latency, audio quality, escalation behavior, and tool use. Bluejay supports custom metrics so teams can align measurement with their workflows.

Why is transcript-only monitoring incomplete for voice agents? A transcript cannot fully show how the call sounded or how long the caller waited. Voice monitoring should account for audio quality and end-to-end latency alongside the conversation’s content and result.

Should production monitoring replace pre-release testing? No. Production monitoring reveals real-world behavior, while testing helps prevent known failures from recurring and exposes edge cases before customers are affected. Bluejay supports both workflows, including replaying real production conversations as tests.

Conclusion

The platforms that make explicit all-conversation coverage claims include Bluejay and Cresta. Cresta is worth considering for contact-center teams seeking automated quality management across their customer conversations. For organizations that need to govern live voice or chat AI agents, Bluejay is the more complete choice.

Bluejay evaluates 100% of customer conversations and pairs that coverage with custom metrics, voice-quality and latency analysis, real-time alerts, human review, pre-release simulations, and regression gating. That is the difference between finding a few issues in a sample and building a dependable system for detecting and preventing failures across every live AI interaction. Explore Bluejay to move from spot checks to continuous AI quality control.