Steno.ai creates custom AI Digital Twins trained on a brand's proprietary content, including videos, podcasts, and documents. This AI twin provides 24/7 empathetic, personalized voice and chat engagement across various digital platforms. The service enables businesses to automatically capture leads, qualify prospects, and drive revenue by scaling personalized interactions without manual intervention.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
- Startup funding source · Source unavailable
Founders
Product
Problem
Many platforms struggle to provide personalized user engagement at scale, leading to overwhelmed users, inefficient support systems, and high churn rates. Users often need instant, tailored support and immediate product value to remain engaged.
Solution
Steno.ai offers a customizable AI chat solution that integrates directly into various platforms, providing personalized user engagement through interactive chat experiences. By embedding an AI assistant directly into their platform, businesses can offer users instant, tailored support and immediate product value. The solution addresses the challenges of user overwhelm and inefficient support by providing a branded AI experience that scales to meet user needs. This allows businesses to improve user retention and drive product success by delivering personalized assistance and guidance.
Target Audience
The primary target audience includes businesses and platforms seeking to improve user engagement, reduce churn, and scale their support systems through personalized AI-driven interactions.
Features
- Customizable AI chat interface that can be themed to match the platform's branding.
- Easy integration with a single code snippet.
- Supports integration into websites, TikTok, Facebook, LinkedIn, X, YouTube, Instagram, Podcasts, Documents, Images, Audio, Video, and Blogs.
- Offers both light and dark themes.
- Iframe-based implementation for easy embedding.