Leveraging Third-Party Reverse Lookup Data in Real-Time AI-Based Voice Channels
AI-powered voice channels are transforming how businesses handle customer interactions, in nearly every aspect. Voice AI agents are used to field calls to restaurants, answer questions at small businesses, and manage customer service for large corporations. The goal of these agents are to streamline operations and ideally, enhance the customer experience.
Unfortunately, this isn’t always the case. While voice AI technology continues to advance rapidly, the challenge of identifying new customers persists. Speech-to-text transcription errors (particularly with names) can derail the personalized experience customers expect, creating frustration and friction.
Why Does Voice AI Struggle with Caller Identification?
Automatic speech recognition (ASR) systems have improved dramatically over the past few years, but they’re still nowhere near perfect. Even in optimal conditions, error rates can be anywhere from 15 to 25%, with accuracy dropping even further for names, accents, and industry-specific terminology.
Through working with customers deploying voice AI, we’ve identified three recurring transcription challenges:
- Spelling Variations: Names like “Lainey” and “Laney” or “Jessie” and “Jesse” often lead to customer record mismatches since the ASR system can’t determine the correct spelling from audio alone.
- Phonetic Similarities: Names that sound alike (e.g., Anna and Ana) frequently get misinterpreted, creating friction when the system pulls the wrong account.
- Parsing Errors: In some cases, ASR systems replace spoken names with unrelated terms or character strings that have no correlation to the actual words spoken, resulting in complete identification failures.
These transcription issues directly impact customer satisfaction. When a voice AI system greets someone with the wrong name or can’t locate their account, the conversation starts on a frustrating note and often requires escalation to a human agent, which defeats the purpose of outsourcing this role to AI.
How Can Third-Party Identity Data APIs Improve Voice AI Accuracy?
AI-based voice applications can integrate third-party phone lookup solutions like Trestle’s Caller Identification API to verify and enrich caller data in real time. These systems cross-reference the incoming phone number against identity databases to retrieve verified customer information, as opposed to relying solely on ASR-transcribed names. Our Caller Identification API returns:
- Full registered name associated with the phone number
- Current address (corroborated from 30+ years of address history)
- Phone metadata including carrier, line type, and prepaid status
- Email addresses associated with the contact
- Demographics for personalization and routing
This data enables voice AI systems to verify transcribed names, fill in missing information, and route calls more intelligently, all before the automated system responds.
Two Approaches to Voice AI Caller Verification
Based on our experience working with voice AI deployments, there are two primary approaches to integrating phone lookup data:
| Initial Customer Experience | Trestle’s Solution | Pros | Cons |
| Greeting-based caller verification: Greet the customer with a message such as, “Hi, our records show this is John Smith calling. Is that correct?” | Query Caller Identification API and return name to be provided back to the customer. | Minimizes input from the consumer and reduces call times. | Data accuracy issues can cause subpar customer experience (around 75% accuracy). |
| Caller identification based on name and phone number query: An example of this type of message is, “Hi, to identify your account, please spell out your first and last name.” | Query Caller Identification API with the name and phone number returned. Accuracy is around 90%. | Accuracy is around 90% If there is no positive match (i.e., for the other 10% of calls), manually collect information. | Adds a layer of friction to the customer and allows for potential transcription issues with spelling. |
In our experience, we recommend Approach 2, caller identification based on name and phone number query. It only takes one bad experience with the customer for businesses to lose confidence in the solution. Gathering an initial name from the consumer before capturing third-party data ensures the most accurate results. Although there is a little more friction, getting the initial name from the consumer to build out the profile is a much better approach, which provides more confidence throughout future stages of the customer journey.
What Data Does Trestle’s Caller Identification API Provide?
Trestle’s Caller Identification API improves caller verification for voice AI by tackling transcription issues at their source. Businesses can significantly reduce transcription-based errors by implementing Trestle’s telephony APIs to create a more seamless caller experience.
You can automate data capture for full name, address, and email, enhancing efficiency and accuracy in real-time interactions. This strengthens customer satisfaction and builds trust in AI-based channels, allowing smooth, efficient, and personalized interactions in small-scale settings and high-volume contact centers.
If you want to know more information about Caller Identification API, check out our API Documentation or contact us.
