カスタマーサービス向けAmazon Q in Connectの更新情報：2025年の概要

Thinking about using Amazon Q in Connect for your contact center? Our 2025 guide is here to give you the real story on the latest updates, what it can do, the setup headaches, and that famously confusing pricing.

Tags:

Amazon Q, Customer Service AI, AI for Contact Center, AWS

Let's be real, the talk about generative AI in customer support isn't just noise anymore. It's actually changing how contact centers work. Teams are on the hunt for tools that can cut through the clutter, solve customer problems faster, and frankly, make agents' lives a little easier. And that brings us to Amazon Q in Connect, AWS's own generative AI assistant for its cloud contact center, Amazon Connect.

On the surface, it sounds great. It promises real-time help for agents, smart self-service bots, and a tight connection with the whole AWS world. But like most things built for the enterprise, the reality is a bit more complicated. Getting it running is definitely not a one-click job, and trying to figure out the pricing can make your head spin.

This guide is here to give you a clear, no-fluff overview of the latest Amazon Q in Connect updates for customer service. We’ll walk through its main features, what it really takes to get it working, and the actual costs you can expect. By the end, you should have a much better idea if it’s the right move for your team.

What is Amazon Q in Connect?

At its core, Amazon Q in Connect is a generative AI assistant that lives right inside Amazon's cloud contact center, Amazon Connect. The easiest way to think of it is as a smart sidekick for both your agents and your customers. Its main purpose is to listen to customer calls and read chats in real time, understand what the customer is trying to do (their "intent"), and then give agents the exact information they need to solve the problem.

And it goes beyond just fetching knowledge base articles. Amazon Q in Connect suggests responses, points to specific actions, and pulls information from all your different knowledge sources, whether that’s your help center, Salesforce, or an internal wiki.

It’s basically the next version of a feature that used to be called Amazon Connect Wisdom, but now it's been beefed up with the powerful large language model (LLM) capabilities of Amazon Bedrock. The whole point is to help your support team be quicker, more accurate, and more consistent, whether someone is a 10-year veteran or it's their first day on the job.

Key features

Amazon Q is built to help out at different stages of the customer journey, from coaching an agent during a live chat to letting customers find their own answers. Here’s a look at its main capabilities.

Real-time agent assistance

This is really the main event for your team. While an agent is talking or chatting with a customer, the AI is working in the background, offering support right in their workspace. It automatically shows suggested replies, links to the right documents, and can even provide step-by-step guides for tricky processes. This is a huge help for getting new agents up to speed and making sure everyone sticks to the correct procedures.

If the automatic suggestions don't quite hit the mark, agents can also just ask Amazon Q questions in plain English, something like, "What's our return policy for orders going to Canada?" It’s like having a product specialist sitting next to every single agent.

There is one pretty big catch, though. To get this real-time agent assistance on voice calls, you have to turn on Amazon Connect Contact Lens, which is a separate tool for conversation analytics. It comes with its own per-minute cost, so while it's a great feature, it's not baked in and will definitely add to your monthly bill.

Generative AI-powered self-service

Amazon Q can also act as the brain for your customer-facing bots, whether they're in your phone system (IVR) or on your website's chat. It uses a system of built-in "tools" to guide the conversation. These tools help it decide the best next move, like answering a question ("QUESTION"), sending the customer to a human agent ("ESCALATION"), or just ending the chat once the problem is solved ("COMPLETE").

You can also create custom tools for more specific jobs, like rescheduling a package or booking a meeting. But this isn't a simple drag-and-drop kind of thing. To build custom actions, you have to get your hands dirty with other AWS services like Amazon Lex (their chatbot builder) and set up complex contact flows. This usually means you'll need someone with some technical know-how.

Knowledge management and integrations

An AI assistant is only as good as the information it can get to. Amazon Q can plug into a bunch of different knowledge sources to find what it needs. You can connect it to third-party platforms like Zendesk, Salesforce, and ServiceNow, or let it learn from internal documents you have stored in Amazon S3. It also has a web crawler that can pull content directly from your public website or help center.

A recent update now gives administrators the ability to choose which LLM to use, including models from providers like Anthropic (the makers of Claude). This gives you a bit more control, letting you pick a model that might be better for complex problem-solving over one that's built for speed.

The reality of the setup process

While the features list sounds pretty good, getting Amazon Q in Connect working is a far cry from a simple plug-and-play setup. It's woven deeply into the AWS ecosystem, which means the setup can turn into a major project, especially if you don't have developers on standby.

Navigating the AWS ecosystem

Just to give you a taste, here’s a high-level look at what it takes to get a self-service bot off the ground:

1. Enable Amazon Q: First, you have to go into your Amazon Connect instance and actually turn the feature on.

2. Configure an Amazon Lex bot: Next, you need to create a bot in Amazon Lex and specifically enable something called the "AMAZON.QinConnectIntent" so it can talk to Amazon Q.

3. Build a contact flow: Then, you have to go back into Amazon Connect and design a visual workflow (a "contact flow") that uses specific blocks like "Amazon Q in Connect" and "Get customer input" to make the bot work.

4. Create routing logic: Finally, you have to add even more blocks ("Check contact attributes") to tell the flow what to do with the AI's output. For example, if the AI decides the customer needs a human, you have to build the path that transfers them to the right agent queue.

This whole process has you jumping between different AWS services, and it can feel pretty clunky and disconnected. It's a stark contrast to solutions like eesel AI, which are built for simplicity. eesel offers one-click integrations with the help desks you’re probably already using, like Zendesk, Freshdesk, and Intercom, letting you get up and running in minutes without writing code or needing an AWS certification.

Challenges with knowledge source integration

Connecting your knowledge sources brings its own set of headaches, especially with the web crawler. It sounds easy enough, just point the AI at your website, but the reality is a lot more technical.

You have to deal with some pretty strict service limits, like a cap of 25,000 files per crawl and 100 source URLs if you use the API. This means you can't just tell it to crawl your entire site. You have to plan carefully, pick out the most important pages, and write some complex rules to tell it what to include or ignore. This usually requires a lot of back-and-forth with your IT team to make sure the crawler doesn’t crash your website, adding another hidden cost to the whole endeavor.

eesel AIは過去のサポートチケットから自動的に学習するため、ウェブクローラーのような複雑な設定は不要です。

This is where a tool like eesel AI really stands out. Instead of making you wrestle with web crawlers, eesel can instantly and automatically learn from your team's most valuable knowledge source: your past support tickets. It figures out your brand voice, common issues, and what solutions actually work by reading real conversations, so it can provide smart, context-aware answers right from the start.

Understanding pricing and limitations

For any support leader, knowing your costs is everything. You need a predictable monthly bill for your tools, but Amazon's pricing model for Connect and Amazon Q can make that incredibly difficult.

Deconstructing the pay-as-you-go pricing model

Amazon Connect gives you two main pricing options: an "unlimited AI" bundle with a higher per-minute or per-message rate, or an à la carte model where you pay for each feature separately. Neither one is particularly straightforward.

The pay-as-you-go model means your bill is tied directly to how much you use the service. A busy month with more calls, longer chats, or a lot of bot interactions can lead to a surprisingly big invoice. The table below simplifies the moving parts, but it's pretty clear that figuring out your final cost is a complicated math problem.

On top of all that, you have to factor in other costs that aren't always obvious. We're talking daily fees for your phone numbers, per-minute charges for the calls themselves, separate fees for every single request your Amazon Lex bot makes, and data storage fees. It all starts to add up, making it a real challenge to forecast your budget.

A simpler alternative: eesel AI

If that pricing model sounds like a headache, that's because it is. This is where eesel AI offers a much more direct and refreshing alternative.

• Predictable Pricing: With eesel AI, the pricing is simple. You can choose from tiered plans based on a set number of AI interactions each month. No weird per-minute fees, no surprise charges. You know exactly what you’re going to pay, which makes budgeting and scaling a whole lot easier.

eesel AIは、複雑な従量課金制モデルとは対照的に、透明性が高く予測可能な料金プランを提供しています。

• Go Live in Minutes: You can forget about that complicated, multi-service setup we talked about. eesel AI is truly self-serve. You can connect your help desk and knowledge sources and launch your first AI agent in under an hour, all without needing to talk to a salesperson or hire a team of developers.

• Test with Confidence: One of the best things about eesel AI is its simulation mode. You can test your AI on thousands of your past tickets to get a real forecast of how it will perform, what its resolution rate will be, and what your ROI looks like before it ever interacts with a live customer. This takes all the risk out of the implementation process in a way that just isn’t possible with Amazon Q.

eesel AIのシミュレーションモードでは、過去のチケットでパフォーマンスをテストし、本番稼働前にROIを予測できます。

• Total Control: eesel AI gives you fine-grained control over your automation from a simple, clean dashboard. You can decide exactly which types of tickets the AI should handle, set its tone of voice with a simple prompt, and create custom actions without needing to be an expert in three different AWS services.

Are the Amazon Q in Connect updates for customer service the right choice?

Amazon Q in Connect is a capable AI tool, there's no doubt about it. But it seems best suited for a very specific kind of company: one that's already all-in on Amazon Connect and the wider AWS world, and has the technical team to handle its complexity.

For most teams, the steep learning curve, tangled setup process, and unpredictable pricing are pretty big barriers. It's a platform that asks for a lot of your time, technical skill, and budget management just to get your foot in the door.

For businesses that want a fast, flexible, and clear AI platform that starts adding value from day one, eesel AI is the obvious choice. It’s designed to improve the help desk you already use, whether that's Zendesk, Freshdesk, or Intercom, without making you go through a painful migration. For support teams that just want to automate repetitive questions, give agents instant answers, and get useful insights without all the operational mess, eesel AI offers a much more practical way forward.

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Sign up for eesel AI and launch your first AI agent in minutes, not months. See for yourself how simple and powerful AI for customer service can really be.