If you’re hunting for an AI platform to manage customer support, you’ve probably run into Yellow.ai.
With the growing need for smart automation, businesses are looking for tools that can handle customer and employee questions without a huge price tag. Yellow.ai presents itself as a big name in this space, offering a single platform that promises to do it all for enterprise service automation.
But what does that actually mean for your team? This guide gives you an honest Yellow.ai overview. We’ll dig into its features, pricing, and some key drawbacks you should know about before you commit. We’ll look at where it does well and where its all-or-nothing approach might be more of a headache than a help.
What is Yellow.ai and how does it work?
Yellow.ai is an “agentic AI” platform built for large businesses to automate conversations over chat, voice, and email. It’s designed as one big system where you can build and run AI agents for both customer experience (CX) and employee experience (EX). The tech behind it uses multiple Large Language Models (LLMs) and its own workflow engine to understand and respond to user requests.
The most important thing to get about Yellow.ai is that it uses a “rip-and-replace” model. This means you’re expected to move your entire support and automation setup onto their platform. You don’t just add it to your current tools; you replace them.
This is a huge commitment. It often comes with high costs, long and tricky setup times, and the real risk of vendor lock-in. Once you’re in the Yellow.ai ecosystem, getting out is tough. This is completely different from more flexible AI tools that add a layer of intelligence on top of the apps you already use, giving you power without the painful migration.
Yellow.ai overview workflow comparison with other implementation models.
A Yellow.ai overview: Key features and platform capabilities
Before we get into the features, just remember that they are all part of Yellow.ai‘s closed system. This gives you a consistent feel across their tools, but you lose the freedom to use other best-in-class apps your team might like better.
Yellow.ai overview: The AI agent builder and development lifecycle
Yellow.ai provides a no-code/low-code space for building AI agents. The main idea is a “super agent” that directs tasks to smaller, specialized agents that handle specific jobs. The platform gives you tools for the entire process, from finding automation opportunities in your data to building workflows, testing, and checking on agent performance.
The agent builder in Yellow.ai.
Yellow.ai overview: Conversational knowledge base and agentic RAG
Yellow.ai‘s brain is its “Agentic RAG” (Retrieval-Augmented Generation) system. This tech connects to a company’s knowledge sources, like websites, internal documents, and APIs, to pull answers. It has features like auto-syncing data and context-aware document chunking to try and make its answers more relevant.
The Agentic RAG process.
Limitation: Any AI is only as smart as the information it learns from. Connecting to a Confluence space or Google Docs is a start, but the real goldmine for any support team is its history of resolved conversations. Past tickets hold the secrets to how your best agents actually talk, solve tricky problems, and deal with unhappy customers. The AI Agent from eesel AI is built to train on this rich historical data, learning not just what the right answer is, but how to say it in your brand’s voice.
Yellow.ai overview: Voice AI and omnichannel support
A big piece of Yellow.ai‘s offering is its voice automation, which they pitch as a replacement for old-school IVR systems. Their platform has features like real-time sentiment analysis to spot when a customer is getting annoyed and support for over 35 channels to create a unified presence everywhere.
Yellow.ai's omnichannel feature.
Limitation: Setting up a proprietary voice system from a single vendor is a massive, expensive, and risky project. For most companies, the biggest wins in automation are in text-based channels like email and help desk chat, where most support conversations happen anyway. eesel AI focuses on mastering these core channels first. It provides huge value by automating replies in your help desk and giving instant answers in tools like Slack and Microsoft Teams, all without the cost and hassle of a complete voice system overhaul.
Yellow.ai overview: Use cases and target industries
Yellow.ai markets itself as a solution for a wide range of business departments and industries.
What it’s mostly used for:
- **Customer Service Automation:** Answering incoming support questions, deflecting common queries, and managing conversations on their supported channels.
- **Employee Engagement:** Building internal bots for IT, HR, and other departments to answer employee questions.
Who they target: Based on their case studies, Yellow.ai focuses on large companies in industries like logistics (Lion Parcel), travel (Pelago), finance (UnionBank), and retail.
The problem with being a jack of all trades: When a tool tries to be for everyone, it can end up not being great for anyone. It might have a lot of features but miss the specific functions that high-performing support teams really need. eesel AI goes a different route with dedicated products like the AI Copilot for agent help and AI Triage for ticket routing. These are built specifically for the unique workflows of customer service and IT support teams.
Yellow.ai overview: Pricing, implementation, and limitations
When you’re looking at a platform like Yellow.ai, you need to look at the total cost, not just the price tag. The money and time you’ll spend on setup are major factors.
The pricing model: A Yellow.ai overview
Yellow.ai‘s pricing is really built for big companies and usually requires a custom quote after you sit through a sales demo. This makes it hard to know what you’ll actually pay. But their listing on the AWS Marketplace gives us a public peek at the investment level.
Pro Tip: Be careful with pricing models that have high starting costs and confusing “conversation limits” or use case caps. This can lead to surprise bills or force you into a more expensive plan as you grow.
| Plan (via AWS Marketplace) | Annual Cost | Description |
|---|---|---|
| Basic Plan | $10,000 | Basic chatbot with 1 use case and tiered conversation limits. |
| Standard Plan | $25,000 | Standard chatbot with 4 use cases and tiered conversation limits. |
eesel AI’s Approach: This kind of enterprise pricing is a non-starter for many teams. The pricing for eesel AI is transparent, affordable, and based on a simple metric: monthly AI interactions. This lets you start small, prove it works, and scale up without a massive upfront payment.
Implementation and paid help: A Yellow.ai overview
Yellow.ai‘s own marketing materials and partner websites often talk about needing professional services and implementation partners. This is a big red flag that setup is going to be complicated and take a lot of work. One G2 review even mentioned a frustrating five-month implementation that still didn’t result in a working product.
eesel AI’s Approach: eesel AI is designed for a fast, do-it-yourself setup. With one-click integrations for over 100 tools, you can connect your knowledge sources and have an AI agent ready to test in minutes, not months. The built-in simulation feature lets you measure the potential ROI on your own past tickets before you go live, taking all the risk out of the process.
eesel AI's easy setup.
A better approach: how eesel AI compares to Yellow.ai
For teams who want AI power without the pain of ditching their entire tech stack, a layered intelligence platform is a better, more modern option. eesel AI is designed to work with your existing tools, not throw them out. It improves the systems you’ve already paid for and that your team actually likes using.
Here’s a straight-up comparison of the two approaches:
| Feature | Yellow.ai | eesel AI |
|---|---|---|
| Platform Model | Monolithic (rip-and-replace) | Layered (integrates with existing tools) |
| Implementation | Complex, often needs professional help | Fast, self-service setup in minutes |
| Best Data Source | Synced documents and websites | Historical ticket data and macros |
| Core Strength | Broad automation for many departments | Deep, accurate automation for CS & ITSM |
| Risk & Cost | High (vendor lock-in, large upfront cost) | Low (transparent pricing, easy to pilot) |
| Flexibility | Low (stuck in one ecosystem) | High (works with all your favorite tools) |
Yellow.ai overview conclusion: Choose a partner that works with you
So, what’s the verdict on Yellow.ai? It’s a big, powerful platform that could work for huge companies that are ready and willing to go all-in with a single vendor. It offers a lot of features under one roof.
But that “rip-and-replace” strategy comes with real downsides in cost, complexity, setup time, and flexibility. For most support teams today, the smarter path is a layered approach. eesel AI gives you powerful, accurate automation by improving the help desk and collaboration tools your team uses every single day. It works with you, not against you.
Instead of planning a painful and expensive migration, you can get an AI agent up and running with your existing setup in just a few minutes.
You can book a demo or start a free trial to learn more.
Frequently asked questions
The platform’s main function is to automate customer and employee support conversations using AI agents. It’s designed as a single, all-in-one system to replace a company’s existing support tools.
The biggest risk is its “rip-and-replace” model, which forces you to migrate your entire setup onto their platform. This can lead to vendor lock-in, high costs, and a complex, lengthy implementation process.
Yes, while official pricing requires a custom quote, the overview notes that public plans on the AWS Marketplace start at $10,000 to $25,000 annually. This indicates it is an expensive solution aimed at large enterprises.
Unlike Yellow.ai‘s replacement model, eesel AI uses a layered approach that integrates directly with the tools you already have. This makes it faster to set up, more flexible, and allows it to learn from your existing historical data for better accuracy.
