Experiment
Online vehicle trade-in funnels often involve long, multi-step forms that can be tedious and error-prone. It is hypothesized that a conversational digital avatar could streamline the process by collecting trade-in data in a more natural and engaging way than a multi-step form. It is also hypothesized that this approach has the potential to reduce errors, increase data accuracy, and generate more precise trade-in valuations while enhancing the overall user experience. For this experiment, I am integrating the robust capabilities of Anam.ai into a high-fidelity, native iOS prototype for validation and user research.
Experiment goals
Integrate Anam.ai digital avatar into high-fidelity MVP Vehicle Passport prototype and gather user feedback and insights.
- Create “Sarah Mitchell,” an AI digital assistant using Anam’s Javascript SDK
- Vibe-code the integration of the Sara Mitchell digital avatar into the Vehicle Passport native iOS 26 experience as a simple simulation for rapid user feedback
- Leverage the native iOS high-fidelity MVP prototype to conduct unmoderated cognitive walkthroughs and complementary mixed-method user research to learn user preferences for either multi-step forms or natural language, voice-driven data capture
Test drive
Meet Sarah Mitchell, Alan Subran’s digital assistant
Anam Lab
For thousands of years, people have connected through natural conversation. Anam.ai is carrying that tradition forward by building AI Personas that make technology feel alive—intuitive, adaptive, and uniquely tailored to you.
Implementation methods
Anam avatars can be integrated into digital experiences using either the very simple and straightforward <iframe> embed feature or, with Anam’s robust Javascript SDK.
Using <iframe> method
- Simple, straight foward implementation method
- Use Anam Lab to build your avatar
- Bring your own custom LLM
- Chat session transcripts downloadable as .txt files via Anam Labs
Using Anam Javascript SDK
- More complex implementation
- Build a persona in Anam Labs
- Bring your own custom LLM
- Call and work with chat session transcripts via API
Working with Anam.ai
Implementing Anam’s digital avatar as an alternative to the multi-step form is likely to provide a more personable and familiar experience.
- Anam was easy to vibe code into the Vehicle Passport native app
- Anam’s avatar seemed very natural during chat sessions and lip and mouth movement was usually synched very well
- The facial and body gestures of Anam’s avatar seemed natural and not forced or spooky
- Easily published a digital avatar and integrated it into the Vehicle Passport app via the native iOS SafariViewController, ensuring microphone permission requests are invoked reliably at runtime
- Providing product and user experience was received warmly and with authenticity. I have found the Anam team to be receptive and sincere about hearing and addressing my concerns and needs
Future state goals
The current state implementation includes a basic simulation of the Anam.ai digital avatar. Future state goals include the following:
- Transcribe the captured conversational transcript and extract important name value pairs that will inform downstream events, data warehouses and processes
- Implement custom LLM capable of navigating complex natural language conversations around specific corpuses of knowledge (e.g vehicles/auto, civil services, skills/professions, restaurants, etc.)