That Was AI vs NINA
Side-by-side comparison of features, pros & cons, pricing, and community votes (2026).
🏆 NINA leads with 345 upvotes
AI phone support that works like a human, for Shopify brands
That Was AI is an innovative AI-powered phone support solution specifically designed for Shopify brands looking to enhance customer service. It mimics human-like interactions, answering customer calls and performing a wide range of support actions such as updating orders, changing shipping addresses, processing cancellations and refunds, creating support tickets, and transferring calls to human agents when necessary. Its quick setup process allows businesses to implement the system within minutes, making it a practical choice for busy e-commerce teams seeking to streamline customer communication without sacrificing quality. By integrating seamlessly with Shopify, That Was AI offers a scalable, efficient, and cost-effective way to handle high call volumes, improve customer satisfaction, and reduce support workload.
Pros
- Human-like AI interaction providing a natural customer experience
- Quick and easy setup process, minimizing onboarding time
- Automates a wide range of support tasks, reducing manual workload
- Seamless integration with Shopify platforms
- Transfers calls to human agents when necessary for complex issues
Cons
- Limited information on customization options and AI accuracy
- Potential limitations in handling highly complex or nuanced inquiries
- No publicly available pricing details, which could affect budget planning
Best for
- • Handling incoming customer calls for order updates and inquiries
- • Processing cancellations and refund requests automatically
- • Updating shipping addresses and order details on the fly
- • Creating support tickets from customer calls for follow-up
Pricing: Likely operates on a subscription-based model with tiered plans, possibly including a free trial or basic tier, and paid plans starting around a few hundred dollars per month depending on call volume and features.
Guide users step by step inside your product.
NINA is an innovative in-product guidance tool designed for B2B SaaS companies aiming to enhance user onboarding and support experiences. Unlike traditional tutorials, chatbots, or static FAQs, NINA lives within the product interface, offering real-time, contextual assistance. Users can ask questions via voice or text when they encounter difficulties, and NINA provides step-by-step guidance directly on the live interface. This approach reduces reliance on support tickets, onboarding calls, and help documentation, streamlining the user journey and fostering higher engagement. NINA is especially valuable for SaaS teams seeking to improve customer success and reduce support workload by empowering users to solve problems independently. Its dynamic, non-scripted nature ensures tailored, relevant support that adapts to each user's specific context, making onboarding more intuitive and support more efficient.
Pros
- Provides real-time, contextual guidance directly within the product interface
- Reduces support tickets and onboarding calls by empowering users to self-serve
- Supports voice and text input for flexible user interaction
- Not scripted, ensuring personalized and adaptive assistance
- Helps improve user engagement and satisfaction
Cons
- Limited information on pricing and deployment specifics
- May require integration effort for some SaaS platforms
- Potential learning curve for teams unfamiliar with in-product guidance tools
Best for
- • Onboarding new users to complex SaaS platforms
- • Reducing support tickets by providing immediate help within the app
- • Guiding users through multi-step workflows or features
- • Assisting users who encounter specific issues or errors
Pricing: Likely operates on a SaaS subscription model, possibly offering tiered plans based on user volume or feature access. Specific pricing details are not publicly available, but similar tools often include a free trial or freemium options with paid plans starting around $50-$200 per month for small to medium teams.