Hundreds of businesses believe that Haptik is more user-friendly and easier to do business with as compared to IBM Watson Assistant
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As per G2, Haptik outperforms IBM Watson Assistant by a massive margin in every category
Over the past 8 years, we have always followed a customer-first approach which also percolates within our teams driving them to provide best-in-class assistance to your business. Right from understanding your business requirements to building a robust Intelligent Virtual Assistant using pre-trained smart skills, team Haptik provides support at every stage of your Conversational AI journey. Haptik ensures that your project goes live as soon as possible and enables you to drive maximum ROI from the product.
Haptik’s dedicated focus of being a product-first company ensures customer needs are always met, often anticipated. We maintain complete transparency with our customers, ensuring that every feature launch, both big and small is well communicated and armed with a stack of robust documentation to reduce the learning curve and train customers better.
Simplicity is key to having a successful product. Over the years, we’ve worked towards making our product easy to use for our customers, and our conversation design studio with in-built pre-trained drag& drop smart skills are a testament to our core belief of keeping it simple. Haptik is specifically designed for CX professionals to be able to build an AI-powered virtual assistant from scratch, with minimal to no coding. We’ve also created an exhaustive library of documentation and how-to articles to aid our customers at every stage of their Conversational AI implementation journey.
Haptik’s biggest differentiator is the product’s Natural Language Understanding. Our NLU architecture is built on a combination of modules such as Language detection, ASR classification, Context Manager, that work in tandem with deep learning-based encoders to accurately understand natural language and handle user queries with higher precision. Haptik has achieved the highest global benchmark for identifying out-of-scope queries (EMNLP 2019 dataset).
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