Overview
Product Information on Yellow.ai Agentic AI Platform
Updated 13th October 2025
What is Yellow.ai Agentic AI Platform?
Yellow.ai Agentic AI Platform Pricing
Overall experience with Yellow.ai Agentic AI Platform
Quality Engineer
50M - 250M USD, Travel and HospitalityFAVORABLE
“Consistent handling of repetitive requests with upfront configuration effort”
SENIOR PRODUCT MANAGER
Gov't/PS/ED <5,000 Employees, EducationCRITICAL
“The Intriguing Contrast Between Ease of Use and Bot Performance”
About Company
Company Description
Located in San Mateo, Yellow.ai specializes in Conversational AI, generating autonomous conversations that appear human-like. Their aim is to expedite enterprise growth through these methods. These conversations form the foundation of brand engagement. Their AI-powered, no-code Dynamic Automation Platform (DAP) is used by multiple enterprises. The proprietary multi-LLM AI engine of DAP, DynamicNLP™ manages around 2 billion conversations from a multitude of channels in numerous languages on a quarterly basis. Yellow.ai aspires to boost customer satisfaction and employee engagement through its Dynamic AI agents that deliver conversational experiences.
Company Details
Company type
Private
Year Founded
2016
Head office location
San Mateo, United States
Number of employees
1001 - 5000
Website
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
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Peer Discussions
Yellow.ai Agentic AI Platform Reviews and Ratings
- Quality Engineer50M-1B USDTravel and HospitalityReview Source
Consistent handling of repetitive requests with upfront configuration effort
We started using Yellow.ai to handle a portion of repetitive customer inquiries that were taking up a significant amount of our support team's time. Initially, we focused on a small set of common requests and gradually expanded from there. One thing that worked well was the ability to handle straightforward cases without requiring agent involvement every time. We also found it useful for keeping responses more consistent across similar situations. The biggest challenge was that conversation flows needed several rounds of testing and adjustments before they matched real customer behavior.
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