So, you're building an AI support agent. You've probably run into the word "embeddings." It sounds super technical, but the idea is actually pretty straightforward. Think of embeddings as the "brain" that helps your AI figure out what customers really mean, not just the words they type. Getting this part right is probably the single most important decision you'll make for getting accurate, helpful answers from your AI.
But let's be real. The world of AI is drowning in jargon, embedding models, vector dimensions, performance benchmarks. If you're a support leader, trying to pick the right one can feel like you accidentally signed up for a data science course. And if you choose poorly? You end up with a bot that spits out wrong answers, frustrates customers, and makes the whole project feel like a failure.
This guide is here to cut through all that noise. We'll walk you through a simple, step-by-step process for choosing embeddings for support content that’s all about business goals, not complicated code.
What you'll need before choosing embeddings
Before we get into the weeds, let's make sure we have a few things sorted out. This isn't about servers or code, but about getting clear on what you're trying to accomplish.
-
Know your number one goal: What's the main job you're hiring this AI for? Is it to handle common questions and deflect tickets? Is it to act as a co-pilot for your agents, helping them write replies faster? Or is it for internal use, helping your team find answers in your company wiki?
-
Map out your knowledge: Where does all your useful information live? Is it neatly organized in a help desk like Zendesk? A wiki like Confluence? Or is it scattered across countless Google Docs and old Slack threads?
-
Define what 'success' looks like: How will you know if this is working? Pick a few concrete numbers to track, like first-contact resolution, average handle time, or your customer satisfaction (CSAT) score.
A step-by-step guide
Here’s a practical way to think through this, broken down into four steps you can actually use.
Step 1: Define your main support automation goal
The "best" embedding model for you completely depends on what you need it to do. Different goals need different kinds of smarts, and a model that's brilliant at one task can be surprisingly clumsy at another.
-
Goal 1: Answering customer questions. If your primary goal is building a bot that can find the most relevant paragraph in your help center to answer a customer's question, you need a model that’s great at "retrieval." These models are specifically trained to match a short, simple query (a customer's question) to a much longer document (one of your help articles).
-
Goal 2: Helping your agents. If you want a tool that suggests similar past tickets to help agents solve a new one, the model needs to be good at "semantic similarity." This just means it's skilled at comparing two chunks of text of about the same length, like two support tickets, and knowing if they're about the same thing.
-
Goal 3: Analyzing incoming tickets. If you're looking to automatically tag new tickets by topic (like "Billing," "Bug Report," or "Feature Request"), you'll need a model that's good at "classification" or "clustering." These models can look at a bunch of conversations and group similar ones together.
Most teams will want to do all of these eventually, but it’s important to know what your top priority is right now. This is a technical detail that a lot of generic AI platforms gloss over, and it's why they often perform poorly, they use the same one-size-fits-all model for every single job.
Step 2: Take a good look at your support content
Alright, next up: take an honest look at the information you're feeding your AI. The type and quality of your content play a huge part in which model will actually work for you.
-
Content type: Is your knowledge in clean, structured help articles from a tool like Zendesk or Intercom? Or is your most valuable info hidden in messy, unstructured places like old ticket histories, Slack DMs, and a bunch of random Google Docs? Models trained on squeaky-clean web text often fall apart when they have to read real support tickets filled with typos and shorthand.
-
Industry jargon: Do you work in a specific field like healthcare, finance, or engineering that has its own language? A general model might not have a clue what your customers are talking about. It probably won't know the difference between "ISO 27001" and "SOC 2," for example, which could lead to some pretty unhelpful answers.
-
Languages: Do you help customers in more than one language? If so, you'll need a multilingual embedding model, which brings a whole new level of cost and complexity to the table.
The model you choose has to be tough enough to handle all your different knowledge sources, not just the perfectly polished articles you show to the public.
An infographic showing how eesel AI can connect to various knowledge sources, centralizing information for better support when choosing embeddings for support content.How eesel AI helps: This is why having a unified platform is such a relief. eesel AI can securely connect to over 100 different places where your knowledge lives, from help desks like Zendesk and Freshdesk to internal wikis like Confluence and Google Docs. Its models are built to understand the real-world messiness of support conversations, jargon and all, so it learns from the information your team actually relies on every day.
Step 3: Weigh the trade-offs: Performance, speed, and cost
Once you've got your goal and content figured out, it's time to weigh the big three trade-offs: performance, speed, and cost. Quick spoiler: you can't have it all. Your job is to find the right balance for your team.
-
Performance (how smart is it?): This is all about how well the model gets the nuances. Can it tell the difference between "I can't log in" and "I need to change my login email?" Bigger, more complex models tend to be smarter, but that comes at a price.
-
Speed (how fast is it?): This is how quickly the model can find an answer. When a customer is using a live chat widget, they expect an answer now, not in ten seconds. Smaller models are usually quicker on their feet.
-
Cost (how much does it cost?): This is what you'll pay to create, store, and search through all the embeddings. Models that capture more detail need more storage and power, which means higher costs, especially as you grow.
Here’s a quick rundown of your options:
| Model Type | Performance (Accuracy) | Speed (Latency) | Cost | Best For... |
|---|---|---|---|---|
| Large Proprietary Models (e.g., OpenAI, Cohere) | Very High | Medium | High | Teams with deep pockets and an engineering squad ready to handle complex APIs and fluctuating costs. |
| Mid-Size Open-Source Models (e.g., BGE, E5) | High | Fast | Low (if you host it) | Teams with their own ML engineers and the server setup to manage and tweak their own models. |
| Small, Fast Models | Medium | Very Fast | Very Low | Niche situations where speed is everything and a "good enough" answer is fine, like simple keyword matching. |
| Managed AI Platforms (e.g., eesel AI) | High | Fast | Predictable | Most support teams. You get high performance without the engineering headaches and a price that doesn't change every month. |
If you decide to pick a model yourself, you're also signing up to manage this tricky balancing act of cost, performance, and infrastructure forever.
Step 4: Test it with your own data
Those public benchmarks you see online? They’re a fine starting point, but they mean very little for your business. They don't know your customers, your product, or the unique ways you talk about things. The only real way to know if a model will work is to test it with your own content.
Trying to do this yourself is a massive, time-sucking project. It usually involves:
-
Creating a "golden dataset": You'd have to manually go through hundreds of real customer questions and match them to the single best answer in your knowledge base.
-
Building an evaluation pipeline: This means writing a bunch of code to test different models against your dataset and measure how well they did.
-
Trying to make sense of the results: You'd be left staring at a spreadsheet of abstract scores, trying to figure out why one model was better than another for a certain type of question.
Frankly, it's a slow, expensive process that requires a data science background.
A screenshot of the eesel AI simulation mode, which helps with choosing embeddings for support content by testing performance on your own data.How eesel AI makes this painless: Instead of asking you to spend months testing, eesel AI has a risk-free simulation mode. In just a few clicks, you can run the AI over thousands of your past tickets in a safe environment. It then gives you a simple report showing what percentage of tickets could have been solved automatically, the exact answers it would have provided, and how much money you could save. You get to see exactly how it will perform with your real data before a single customer ever talks to it. It’s a level of confidence you just can't get anywhere else.
Common traps to avoid
-
Falling for the benchmark hype: Public leaderboards are generic. Your support content isn't. A model that looks great on paper might be a total flop with your data.
-
Ignoring the total cost: The price per API call is just one small piece of the puzzle. You also have to factor in the cost of your engineers' time to build, monitor, and maintain the whole system.
-
Forgetting about speed: An incredibly accurate answer that takes 30 seconds to arrive is useless. Customer-facing bots need to feel instant, otherwise, people just won't use them.
-
Using one tool for every job: As we covered earlier, the best model for finding a document is different from the best one for tagging a ticket. Using the wrong tool will always lead to disappointing results.
The simpler path: Letting a platform do the heavy lifting
Let's be honest: choosing, implementing, and fine-tuning embedding models is a full-time job for a team of machine learning engineers. For most leaders in support, success, or ops, trying to build it yourself is just too complex, expensive, and risky.
This is exactly why integrated AI platforms were created. eesel AI handles the entire challenge of choosing embeddings for support content for you. Our platform was built from day one to automate customer support and internal help desks. We manage the model selection, the data connections, and all the performance tuning so you can focus on what you do best: giving your customers and team better, faster support. You can set it up yourself in minutes, not months, and connect it to your existing tools without needing a developer.
Wrapping up
Choosing the right embeddings is the foundation of a successful AI support strategy. By thinking clearly about your goals, your content, and the trade-offs between performance, speed, and cost, you can make a smart choice. And while you could go down the long, complicated DIY road, platforms like eesel AI offer a powerful and simple way to get all the benefits of world-class AI without needing to become an expert in it.
Ready to see it in action? Sign up for a free eesel AI trial and you can launch your first AI agent in under 5 minutes.
Frequently asked questions
Before diving deep, you should clearly define your primary AI support goal, map out where all your support knowledge lives, and establish concrete metrics for what success looks like. This foundational understanding guides all subsequent decisions.
Your goals directly determine the type of model needed; for example, answering customer questions requires a retrieval model, while helping agents find similar past tickets benefits from a semantic similarity model. Different goals demand different AI capabilities.
The type and quality of your content, including its structure, industry-specific jargon, and supported languages, are crucial. Models trained on clean data might struggle with messy, real-world support conversations or highly specialized terminology.
You must balance performance (accuracy), speed (latency), and cost. Generally, higher performance models can be slower and more expensive, so finding the right equilibrium for your specific needs is essential.
Absolutely. Public benchmarks are generic and won't reflect your unique customer interactions or product-specific jargon. Testing with your actual support data is the only reliable way to assess a model's real-world effectiveness for your business.
Avoid being swayed solely by public benchmarks, underestimating the total cost (including engineering time), neglecting the importance of speed for customer-facing applications, and using a single, one-size-fits-all model for diverse tasks.
For most support leaders, a managed AI platform simplifies the process significantly by handling model selection, data connections, and performance tuning. Building it yourself is a complex, time-consuming task best suited for dedicated machine learning engineering teams.
