How Much Setup Do Generative AI Auto QA & Auto Data Redaction Require?

Adopting a new technology can be intimidating, especially if it represents such a fundamental leap as Artificial Intelligence (AI). Since the introduction of our new generative AI-powered Auto QA and Auto Data Redaction solutions, we have often received the following question: "How out-of-the-box are they, or in other words, how difficult it is to set them up?"

In this article, we will have a closer look at what you can expect when setting up AI-powered Auto QA and Auto Data Redaction by MiaRec, what skill sets and resources you might need, and when you can expect to start using the features.

Auto Data Redaction: Zero Configuration For Redacting Payment & Credit Card Numbers

MiaRec's AI-powered Data Redaction feature classifies Personally Identifiable Information (PII) and renders it automatically unrecognizable. It offers two major benefits to contact centers:

• It helps to achieve compliance, and
• It limits the damage caused by data breaches or theft as all payment card numbers are redacted.

Less Setup Compared To Pattern Matching

MiaRec's Auto Data Redaction uses cutting-edge Named Entity Recognition (NER) and Machine Learning (ML) technology. This makes it much more accurate than traditional pattern-matching. It can better comprehend the context of conversations to accurately determine what is classified as Personally Identifiable Information (PII) and what isn't, removing the need for complicated queries and time-consuming setup, and significantly reducing the level of missed redactions and over-redactions.

Zero Configuration For Common Scenarios

MiaRec's Auto Data Redaction ships ready out-of-the-box (zero configuration required) with a Named Entity Recognition (NER) model trained on payment card numbers, which means it will recognize debit and credit card numbers, saving valuable time and effort during the initial audit and fine-tuning phase.

Train Your Custom Models 

Also, customers with unique data redaction needs, like social security numbers, patient ID numbers, or other PII redaction requirements, can train their own NER model based on their data. 

Training a model requires the preparation of training data. Such a process is somewhat time-consuming, but it is well worth it. A custom model achieves high accuracy because it is tailored to the customer's unique call scenarios.

Human annotators need to manually listen to approximately 500 call recordings and mark the regions in the conversation with the appropriate label. Examples of labels are "credit card", "phone number", "social security number," and "patient name". To make this process seamless for annotators, MiaRec provides a built-in annotation tool specifically designed for annotating call recordings.

Once the training data is prepared, it is a matter of a few clicks to start a model training. MiaRec hides all the complexities of machine learning from end users. No special data engineering skills are required. The trained model will be available for usage right away.

Summary: For credit card redaction needs, you can use MiaRec's built-in data redaction model right away without any additional configurations. For other redaction use cases, you can easily train your custom NER model using the tools MiaRec will provide you with. Depending on the complexity of your redaction needs and how many resources you can utilize to train the model, you can be up and running within one to two weeks. 

Auto QA: Score 100% Of Your Calls Right Out Of The Box

MiaRec's new AI-based Auto Quality Assurance (QA) marks another major leap in contact center productivity and performance. Generative AI-based Auto QA makes the agent evaluation process much more efficient and cost-effective as it gives you the ability to score 100% of your calls.

Unlike traditional keyword-based Auto QA solutions, MiaRec's Generative AI-powered Auto QA is immediately ready to use without any configuration. Since the new Auto QA solution is so intuitive and easy-to-use, there is no need for data analysts or AI engineers. Contact center supervisors can copy and paste their evaluation questions right into the scorecard, turning them into AI prompts. MiaRec will then evaluate the entire conversation transcript to find answers to questions like "Did the agent greet the caller appropriately?", "Did the agent resolve the issue?" etc.

Although the accuracy is already very high and improves at a mind-boggling pace, AI may return incorrect answers in some hard cases where human judgment is necessary. If you find that your Auto QA consistently scores something wrong, you may need to reformulate the question to create a slightly different prompt or remove the question from Auto Scoring and include it in your manual deep-dive evaluations.

Summary: Businesses can immediately take advantage of the full potential without any hassle. The user-friendly interface enables easy access to valuable customer insights, allowing businesses to leverage them effectively.

Conclusion

In conclusion, adopting new technologies like Artificial Intelligence (AI) can be daunting, but MiaRec's AI-powered Auto QA and Auto Data Redaction solutions make the process seamless and user-friendly. With zero configuration required and no need for specialized skills, contact centers can immediately take advantage of the benefits these solutions offer.

Auto Data Redaction provides compliance, security, and an improved employee experience, while Auto QA allows for 100% call scoring and valuable customer insights. Whether you need redaction for payment card numbers or have unique redaction requirements, MiaRec's tools cater to your needs. So why wait? Engage further with MiaRec and unlock the full potential of these AI-powered solutions for your business.