NextDimensionAI ships safer, faster healthcare voice agents

NextDimensionAI uses its proprietary simulation building system that creates scenarios based test cases in Hamming to test real-life scenarios. These include hundreds of scenarios that reflect real patient behavior, ranging from straightforward appointment requests to incomplete information, pauses, repeated questions, or unexpected call patterns.

Scenario coverage is not limited to clean, idealized flows. The team intentionally creates cases that mimic real patient behaviour, long pauses, interrupted speech, and noisy environments.

These qualitative factors were impossible to capture consistently during manual calling. Engineers tag each test and then run large batches in parallel. Instead of making manual calls one by one, they can execute an entire suite at once and immediately see:

• Which scenarios pass or fail
• How prompts change or affect LLM behavior
• How the agent handles variations in noise, pauses, or accents

Hamming has become part of their default engineering workflow. The team treats Hamming runs the same way a software team treats unit tests

NextDimensionAI also uses Hamming to benchmark the impact of configuration changes. They run controlled tests to measure latency effects from:

• Different compute regions
• PSTN providers
• LLM temperature and response settings
• TTFW adjustments

Through these controlled tests, NextDimensionAI identified a configuration that reduced latency by 40%.

Hamming's testing suite allows NextDimensionAI to run hundreds of synthetic calls under controlled conditions, and they can quantify the impact of each configuration change with precision.

For example, the team measured how small fluctuations in TTFW affected patient perception, or how different PSTN carriers performed under load. It also gave them historical reference points, so as new models, telephony providers, or infrastructure environments became available, they could quickly evaluate whether switching would be beneficial.

Production calls are monitored continuously by internal QA tools and supervisor agents. When a live call fails, the team reviews it and converts it into a new Hamming test case.

Every improvement goes through two steps:

• Run the new post-production test case
• Re-run all original pre-production tests

This ensures incremental improvements don't break existing functionality. Over time, the test suite grows to reflect real patient behavior.

NextDimensionAI's QA loop blends automated evaluation with human-in-the-loop (HITL) review. Their supervisor agents label calls based on intent accuracy, conversation quality, and completion outcomes.

When a call is flagged, a forward deployed AI Engineer reviews AI agent performance, identifies the root cause, and formalizes it as a reproducible Hamming test. This means the entire organization learns from every real failure.

Over time, this has created a comprehensive library of real-world cases: patients with strong accents, callers who provide too much or too little information, prescription issues, or behavior that falls outside any scripted flow. Each of these becomes a test the agent must pass before any future release.

Since NextDimensionAI operates in healthcare, HIPAA requirements are built directly into their test cases. The team creates scenarios that explicitly check whether an agent is acting in compliance with HIPAA. NextDimensionAI also tests for conversational edge cases, such as callers attempting to bypass verification by volunteering extra details, or asking the agent to read back medical history.