AI is everywhere these days, right? Especially in customer service. In fact, did you know that about 70% of folks leading customer experience teams are planning to bring generative AI into their customer chats and emails pretty soon? That’s a big shift, and it’s really pushed companies to create smart tools that can automate support using AI. And when you talk about AI for support, one name that pops up is Decagon AI.
In this post, we’re going to dive into what Decagon AI is all about, what its main features are, where it might not be the perfect fit for everyone, how its pricing works (or doesn’t, as we’ll see), and explore some of the best alternatives out there, including how eesel AI stacks up. While Decagon has certainly made a name for itself, other solutions offer different advantages that might be a better fit depending on what you need and how you run your support show.
What is Decagon AI?
So, what exactly is Decagon AI? It’s a startup that focuses on providing advanced generative AI solutions specifically for customer support teams at bigger companies. Their main goal is to use powerful AI models (called LLMs) to automate customer service tasks. Jesse Zhang and Ashwin Sreenivas started Decagon in 2023, and they’ve already gotten a good chunk of investment from top venture capital firms, which just shows how much interest there is in this kind of tech.
The platform is built using fancy natural language processing (NLP) and machine learning tech. They use foundational AI models from big names like OpenAI, Anthropic, and Cohere. Then, they layer on top of that with your company’s specific data – things like your help center articles and past customer conversations. Decagon aims to create “human-like AI agents” that can really understand what customers are asking, guess what they might need next, and give personalized answers right away, almost like a human agent would.
Decagon AI’s core features
Decagon AI comes with a set of features designed to automate and improve how you handle digital customer service chats and emails. Here’s a look at some of the key things it can do:
- Smart, context-aware replies: Generates accurate, personalized responses by pulling info from past chats, customer details, and current history.
- Learns as it goes: Improves over time by learning from customer interactions to better understand intent and handle new questions.
- Handles multiple channels at scale: Connects with chat, email, and voice to manage conversations across platforms for a consistent experience.
- Provides helpful insights: Offers analytics via Watchtower to monitor AI agent performance, spot trends, and identify training needs.
- Automates tasks for efficiency: Automates repetitive tasks and performs complex actions like processing refunds or cancellations via API connections (e.g., with Stripe). Stripe even shared a story about using Decagon for this.
These features position Decagon AI as a tool focused on automating customer interactions and handling a large volume of requests, especially the routine ones.
Decagon AI pricing explained
One thing you’ll notice right away about Decagon AI is that they don’t seem to share their pricing publicly. You won’t find pricing tiers on their website, and details aren’t easy to find elsewhere. This can make it a bit tricky if you’re trying to figure out your budget or compare them to other options.
However, reports suggest Decagon uses pricing models based on how much you use the service. These typically include:
- Per-conversation pricing: This is a set fee for every single interaction the AI handles, even if it doesn’t actually solve the customer’s problem.
- Per-resolution pricing: This rate is higher, but you only get charged when the AI agent successfully solves the customer’s issue without a human needing to step in.
Here’s a quick look:
| Pricing Model | Basic Description | Potential Downsides |
|---|---|---|
| Per-conversation | Flat rate per AI interaction | Costs can add up fast, less incentive for the AI to fully resolve issues |
| Per-resolution | Higher rate, only on successful AI resolution | Can be unclear what counts as a “resolution”, makes costs hard to predict |
| eesel AI | Transparent, interaction-based, includes usage amount | None (it’s designed to be predictable and provide clear value) |
While the per-resolution model sounds like you only pay for success, the context notes that most teams end up using the per-conversation pricing. Why? Because it’s simply easier to predict costs, even if figuring out what counts as a “resolution” can be debated.
The main problem with this not-so-transparent, usage-based approach is that it can be unpredictable.
- Unpredictable costs: The lack of clear starting rates, usage limits, or examples makes budgeting difficult, with costs potentially rising quickly during busy periods.
This is quite different from the clear, interaction-based pricing model you get with eesel AI. eesel AI offers straightforward pricing tiers based on how many interactions you have, which makes costs predictable and avoids surprise fees. There’s no extra charge per agent, making it a budget-friendly way to boost your support team’s productivity as you grow. This clarity means businesses can confidently plan their spending and measure how much value they’re getting.
Where Decagon AI might not be the perfect fit
Now, while Decagon AI has a cool vision for automating customer support, the info available suggests there are a few spots where it might not work perfectly for every business. This is especially true for companies with complicated setups or those who rely on a mix of AI and human agents working together.
- Limited integration with workforce management tools: Doesn’t connect deeply with tools for managing human agent schedules, queues, or workload visibility, making coordination difficult in mixed teams.
- Difficulty handling big spikes in demand: Reports suggest the platform may slow down during busy periods, leading to slower replies, errors, or unexpected escalations to human agents.
- Tricky AI-human collaboration: Smoothly handing off conversations to human agents can be difficult, with potential loss of context forcing customers to repeat themselves.
There also seem to be some tricky spots in how AI and humans work together. While Decagon aims for the AI to solve issues on its own, handing off a conversation smoothly to a human agent can be tough. Sometimes, the important details might not carry over completely when a ticket is escalated, forcing customers to repeat themselves or agents to dig back through the history. If it’s not clear how and when to hand off, it can cause delays and frustrate both customers and your team. This is a contrast to how important it is for AI and humans to work hand-in-hand in today’s support world.
Ultimately, the info suggests Decagon AI’s design is optimized for speed and letting the AI run on its own. This sometimes means it focuses on automating individual tasks rather than coordinating everything. While it seems good at handling specific jobs, it might not have the depth needed to manage people, channels, workflows, and systems all together in complex, mixed support environments. And coordinating all those pieces is where many businesses find the biggest benefit.
These potential limitations point to common issues that flexible AI solutions like eesel AI are built to handle. eesel AI focuses on integrating smoothly, performing reliably, and making sure AI and humans work well together.
Alternatives to Decagon AI
Decagon AI is definitely one of the names you’ll hear in the AI support world, but it’s not the only choice out there. And for many teams, it might not be the most complete one. Other platforms have different strengths that could be a better fit depending on how your operations run. For example, Voiceflow is known for helping teams build custom AI experiences quickly, while Assembled is highlighted for its strong foundation in workforce management and helping you coordinate your operations.
eesel AI stands out as a powerful alternative, especially for businesses that need flexibility, deeper connections with their existing tools, and costs they can predict, all without missing out on advanced AI features. While Decagon might be great at automating specific tasks, eesel AI is built to fit right into your current support setup and improve workflows in a way you can customize more easily.
Here’s how eesel AI helps with some of the things where platforms like Decagon might fall short:
- Flexible Training: Trains bots on 100+ sources (past tickets, internal docs, help centers, etc.) with auto-sync for the latest info.
- Customizable Actions and Workflows: Allows bots to perform actions like grabbing customer info, handling refunds, or updating accounts using Custom API Actions with detailed control.
- Smooth AI-Human Handoff: Designed for mixed teams with full context transfer during handoffs and an AI Assistant/Copilot browser extension for human agents.
- Predictable Pricing: Offers clear, interaction-based pricing with no per-agent fees, ensuring predictable costs as you scale.
- Solid Testing Tools: Provides tools to test bot responses using past tickets, fine-tune behavior, and roll out selectively to agents/teams.
Basically, eesel AI is built to connect deeply with the tools and workflows you already use. It offers a flexible, cost-effective, and highly customizable AI solution that works with your human team, not just trying to replace them. You can learn more about eesel AI‘s approach at https://eesel.ai.
Decagon AI vs. eesel AI: A comparison
Let’s quickly stack up Decagon AI and eesel AI on a few key points:
| Feature | Decagon AI | eesel AI |
|---|---|---|
| Pricing Model | Not public, usage-based (per-convo/per-resolution) | Clear, interaction-based, no per-agent fees |
| Training Sources | Help centers, past convos, company data | 100+ sources (past tickets, internal docs, help centers, etc.), auto-sync |
| Customization | Basic tone, AOPs | Detailed tone control, custom prompts/actions, supports multiple bots |
| AI-Human Handoff | Can be tricky, context might get lost | Smooth, context stays with the ticket, AI Assistant/Copilot helps agents |
| Workforce Integration | Limited | Connects with helpdesks, supports agent workflows, provides insights |
| Testing | Limited pre-launch | Simulation on past tickets, selective rollout, browser extension testing |
| Actionability | Can do actions via API | Custom API actions, e-commerce actions, flexible workflow automation |
This comparison shows that while Decagon AI focuses heavily on letting the AI work on its own, eesel AI puts more emphasis on being flexible, connecting with your existing tools, and making sure AI and human agents work together seamlessly. Plus, it does this with costs that are clear and predictable.
Is Decagon AI right for your business? (And when to consider alternatives)
So, is Decagon AI the perfect fit for you? It seems like it could be a strong option for fast-growing, digital-first companies that handle a lot of support requests and really want to automate routine, simple tasks. Companies like ClassPass, which reportedly used Decagon to handle millions of conversations and cut costs significantly, show how well it can work when your main goal is to quickly automate common questions because you have huge volume. If your biggest headache is deflecting tons of simple tickets and you’re okay with the AI working mostly on its own, Decagon could be a good choice and which eesel AI can easily do.
However, if your support setup is complicated, requires close teamwork between AI and human agents, deals with unpredictable surges in how many requests you get, needs deep connections with tools that manage your team’s workload, or if you really need pricing that’s clear and predictable, Decagon’s potential limitations might become significant problems. When smooth handoffs, flexible ways of working, a full view of your operations, and predictable costs are high priorities, it makes sense to look at alternatives that are built for coordinating everything and working in mixed human-AI environments. eesel AI is designed specifically for these kinds of situations, offering the flexibility and control you need for more involved support operations.
Finding the right AI support partner
Choosing the right AI support platform isn’t just about picking a tool that can answer questions. It’s really about finding a partner that fits how you run your operations, how complex your workflows are, and what your budget looks like. Decagon AI is a notable player that focuses on automating customer conversations, but it’s important to look closely at its features, where it might fall short, and its pricing structure compared to what you specifically need.
If you’re looking for an AI solution that fits right into your existing helpdesk, offers pricing you can count on, and gives you the flexibility needed for complex, mixed human-AI support environments, eesel AI might be just what you need.
You can check out what eesel AI can do by starting a free trial or booking a demo to see firsthand how it can improve your customer support workflows.
