The 7 best knowledge retrieval tools for support teams in 2025

We’ve all been there: knowing an answer exists somewhere but having no clue where to find it. It’s frustrating. Now, picture your support team living that reality every single day. Agents spend nearly 20% of their week just digging for information across scattered wikis, old tickets, and random Google Docs. It’s a massive time sink that slows down resolutions and leaves everyone, agents and customers alike, feeling stuck.

In 2025, customer expectations for instant, accurate support are higher than ever. You can't afford to have your team scrambling for answers. They need a system that doesn't just point them to a 50-page manual but gives them the exact answer, right when they need it.

That’s what knowledge retrieval tools are for. I’ve spent some time digging into the top options to see which ones actually hold up. This post breaks down the 7 best tools out there, comparing what they’re good at, where they fall short, and who they’re really for, so you can pick the right one for your team.

What are knowledge retrieval tools?

Simply put, knowledge retrieval tools are systems designed to find and deliver specific information from your company's entire collection of data. Think of it as a super-smart search engine built just for your internal content.

But modern tools do a lot more than basic keyword searching. Most now use a technology called Retrieval-Augmented Generation (RAG). All that means is the AI doesn’t just find a relevant document and toss it over. Instead, it reads the important snippets of information, understands what you’re asking, and then writes a direct, human-like answer, usually pointing to the exact source it used.

This workflow illustrates how knowledge retrieval tools automate the support process, from initial query to final resolution.

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The whole point is to get rid of manual searching, giving your agents precise answers directly within the platforms they already use every day, like their helpdesk.

How we evaluated the best knowledge retrieval tools

Not all tools are the same, and what looks amazing in a demo can be a real headache to implement. My evaluation focused on the things that actually make a difference to a support team’s daily work and your company's bottom line.

• Ease of setup: How quickly can you get it working? Does it require a team of developers and endless sales calls, or can you get started on your own in a few minutes?

• Integration depth: Does the tool connect smoothly with the helpdesk and knowledge sources you already have, like Confluence, Google Docs, and past tickets? Or does it make you migrate everything?

• Customization and control: Can you actually tweak how the AI behaves? Can you define its personality, tell it what topics it can’t answer, and connect it to other tools to do things, like looking up order details in Shopify?

• Testing and reporting: Is there a safe way to test how the AI will perform with your real data before you let it talk to customers? Can you see what’s working and what isn’t?

• Pricing transparency: Is the pricing simple and predictable? Or are you going to get a surprise bill after a busy month because they charge per resolution?

Comparison of the top knowledge retrieval tools for 2025

For a quick look, here’s how our top 7 knowledge retrieval tools stack up against each other based on those criteria.

Tool	Best For	Ease of Setup	Key Integrations	Pricing Model
eesel AI	All-in-one support automation	Self-serve (minutes)	Zendesk, Slack, Confluence, G-Docs	Flat-rate subscription
Coveo	Enterprise-level search	Complex (months)	Salesforce, ServiceNow	Custom (Quote-based)
Bloomfire	Centralized knowledge management	Moderate	Most CRMs, Slack	Per user/month
LlamaIndex	Technical teams building custom RAG	Developer-heavy	N/A (framework)	Open-source & Usage-based
Relevance AI	AI agent and workflow building	Moderate	Hubspot, Zapier	Usage-based
Haystack	Open-source search pipelines	Developer-heavy	Elasticsearch, FAISS	Open-source
OpenAI Assistants API	Custom developer projects	Code-intensive	N/A (API)	Usage-based (tokens)

The 7 best knowledge retrieval tools

1. eesel AI

eesel AI is an AI platform built specifically for customer service and internal support. It’s made to plug directly into your existing tools like Zendesk, Slack, and Confluence to bring all your scattered knowledge together. It also learns from the incredibly valuable information in your team's past ticket conversations. It really stands out because you can set it up yourself, and its workflow engine is powerful without needing a developer to manage it.

An infographic showing how eesel AI acts as a central hub, integrating various knowledge sources to provide unified answers.

Why we included it: It’s the only tool on this list that offers serious power in a package you can set it up yourself in minutes, not months. The ability to simulate its performance on thousands of your own past tickets before going live gives you a level of confidence no other tool can offer.

Pros:

• You can actually sign up and get it working without ever talking to a salesperson.

• It connects to all your knowledge sources, especially the useful context hidden in old tickets.

• The simulation mode lets you test everything and forecast your ROI without any risk.

• The pricing is a transparent, flat-rate subscription, so you don't get punished for being busy.

Cons:

• If you're just looking for a simple search bar for your documents and nothing else, its full suite of automation tools might be overkill.

Pricing:

eesel AI's pricing is based on a flat monthly fee that includes all its core products.

A screenshot of eesel AI's pricing page, highlighting its transparent, flat-rate subscription plans.

• Team Plan: $299/month ($239/month on an annual plan) for up to 3 bots and 1,000 AI interactions.

• Business Plan: $799/month ($639/month on an annual plan) for unlimited bots, 3,000 AI interactions, and unlocks key features like training on past tickets and AI actions.

• Custom Plan: For unique setups with unlimited interactions and advanced features.

2. Coveo

Coveo is a big, enterprise-level AI search and recommendation platform. It’s built to handle and deliver relevant information across huge, complicated corporate systems, including public websites, company intranets, and large-scale customer service operations.

Why we included it: Coveo has been in the enterprise search business for a long time and is a popular choice for massive companies with complex needs and the budget and technical team to handle it. Its analytics and personalization engines are very powerful.

Pros:

• Scales well for huge, global organizations.

• Offers very deep analytics and reporting features.

• Strong personalization capabilities to tailor experiences.

Cons:

• The setup process is known for being long, complicated, and requires a dedicated technical team.

• The pricing is not public and is quote-based, making it out of reach for most small to medium-sized businesses.

• It's the opposite of self-serve; you can’t do much without going through their sales and implementation teams.

Pricing:

Coveo’s pricing is entirely custom. You can only get it after going through a sales demo and consultation. There are no public pricing tiers, which can be a real roadblock for teams that need to move quickly.

3. Bloomfire

Bloomfire is a platform focused on knowledge management and sharing. Its main purpose is to help companies create a single, central place for all their information, making it easy for employees to search and access. It uses its own AI-powered search to help users find content within the Bloomfire system.

Why we included it: If your main goal is to build a centralized knowledge hub from the ground up and encourage a culture of content creation, Bloomfire is a solid choice. Its features are all about creating, organizing, and sharing knowledge internally.

Pros:

• Strong focus on content curation and knowledge-sharing.

• Its AI search can index content like video and audio transcriptions.

• The interface is user-friendly, especially for people who aren't developers.

Cons:

• It's less focused on integrating with and learning from external sources where your knowledge already exists, like your past helpdesk tickets.

• It’s a "destination platform," meaning you have to go to Bloomfire to find info. This can sometimes create another silo if not everyone on the team uses it consistently.

• It doesn't have the advanced automation and workflow features of more modern, integrated AI platforms.

Pricing:

Bloomfire doesn't list its pricing publicly. You have to request a demo and get a custom quote based on how you plan to use it (for a single team, a department, or the whole company).

4. LlamaIndex

LlamaIndex is a popular open-source data framework for building applications that use Large Language Models (LLMs). It’s important to know that it’s not a ready-to-use tool but a library for developers to connect LLMs with their own data. It gives you the building blocks to create your own custom indexing and RAG systems.

Why we included it: For technical teams with dedicated developers, LlamaIndex offers total flexibility. You can build a completely custom knowledge retrieval system from scratch, tailored to your exact needs.

Pros:

• Extremely flexible and can be customized for just about any use case.

• It’s open-source, so the framework itself is free.

• Integrates with a huge ecosystem of data sources, LLMs, and vector databases.

Cons:

• Requires serious coding and AI expertise to set up, maintain, and scale.

• It has no user interface, analytics, or workflow engine; you have to build all of that yourself.

• The total cost can get very high when you add in developer salaries and cloud hosting fees.

Pricing:

The LlamaIndex framework is open-source and free. They do offer a managed service called LlamaCloud with usage-based pricing.

• Free Tier: Includes 10,000 credits per month.

• Starter Plan: $25/month for 50,000 credits.

• Pro Plan: $250/month for 500,000 credits.

• Credits are used for things like parsing and indexing data, and 1,000 credits equals about $1. This model can get unpredictable as your usage grows.

5. Relevance AI

Relevance AI is a platform with tools for building and deploying custom AI agents and workflows. It takes a more visual, low-code approach, letting users string together different AI models and data sources to do specific tasks, including knowledge retrieval.

Why we included it: It hits a nice middle ground for semi-technical teams. If you want more control than a fully managed platform like eesel AI but don't want to build everything from scratch with a library like LlamaIndex, it’s worth checking out.

Pros:

• A visual, low-code interface makes building AI workflows more accessible.

• Flexible enough to be used for many things beyond customer support.

• Good for teams that like to experiment with connecting different AI tools and APIs.

Cons:

• It's harder to learn than dedicated support tools, and your workflows can get complicated fast.

• Pricing is usage-based, which can lead to unpredictable and high costs as you grow.

• It's missing support-specific features like deep helpdesk integrations and ticket simulation.

• Note: At the time of writing, both the main website and pricing page for Relevance AI were down due to a server error, which might be a concern for reliability.

Pricing:

Relevance AI has typically offered a free tier with usage-based paid plans. However, with their site currently down, up-to-date pricing details aren't available.

6. Haystack

Haystack is another open-source framework, from a company called deepset, for building natural language processing applications, with a strong focus on search systems. Like LlamaIndex, it’s a toolkit for developers to build their own systems, but it’s especially good for creating search pipelines that are ready for production.

Why we included it: Haystack is a mature and powerful option for technical teams that want to build and own their entire search infrastructure. It offers solid, modular components for everything from data processing to retrieval and question answering.

Pros:

• Highly modular and designed for production environments.

• Supports different retrieval methods, including traditional keyword search and modern vector search.

• Backed by a strong company (deepset) and an active open-source community.

Cons:

• It is purely a developer framework; there's no UI for non-technical users to manage or configure the system.

• Requires deep technical knowledge of search systems, NLP, and MLOps.

• The focus is more on the "retrieval" part of RAG than the generative AI and automation that comes after.

Pricing:

The Haystack framework is open-source and free. deepset also offers deepset Studio, a UI for building pipelines, which has a free tier for individual developers. A full Enterprise plan with unlimited usage and support is available with custom pricing.

7. OpenAI Assistants API

The Assistants API from OpenAI has a powerful "File Search" feature, which is basically RAG-as-a-service. It lets developers upload files directly to OpenAI, which then handles all the complicated backend work of parsing, chunking, and retrieving information to improve the responses of models like GPT-4o.

Why we included it: This is the most direct way to use the latest RAG technology from one of the world's top AI labs. For teams building a custom application, it's a key component.

Pros:

• You get access to the newest technology from the creators of ChatGPT.

• It automatically handles many of the most difficult and tedious steps of building a RAG pipeline.

• It's relatively easy for a developer to integrate into any application.

Cons:

• It is just an API. You have to build the entire application, user interface, integrations, and reporting yourself.

• The usage-based pricing can get very expensive, very quickly, especially with large knowledge bases and lots of queries.

• It lacks any of the management features, workflow tools, or analytics needed to run an effective support operation.

Pricing:

The pricing is usage-based and has two main parts:

• API Calls: You pay per token for both the input and output of the model you use (e.g., GPT-4o).

• Vector Storage: You pay for storing your files. After a small free tier, it costs $0.10 per GB per day. This can add up to a big and unpredictable monthly bill.

Tips for choosing the right knowledge retrieval tools

Feeling a bit overwhelmed? Here are a few practical tips to help you cut through the noise and find the right fit.

• Start with your existing workflow. Don't pick a tool that forces you to ditch your helpdesk or wiki. The best solutions plug into the tools you already have. This causes less disruption for your team and makes adoption much faster.

• Prioritize speed to value. Ask yourself: "Can I try this out today, by myself?" A tool that requires multiple sales calls and a six-month implementation project is a huge gamble. A self-serve tool lets you prove its value in an afternoon.

• Look beyond the official knowledge base. Some of your most useful, real-world knowledge is hidden in your team's past conversations. A tool that can learn from historical support tickets will understand your brand's voice and your customers' actual problems much faster than one that only reads polished help articles.

• Demand a risk-free trial. The only way to really know if a tool will work for you is to test it on your data. Look for tools that offer a simulation mode or a proper free trial so you can see its actual performance before you commit any money.

The right knowledge retrieval tools unify your knowledge and help your team

Choosing the right knowledge retrieval tool is about more than just finding documents faster. It’s about breaking down the information silos that slow your company down. It’s about giving your agents instant, accurate answers so they can solve customer problems with confidence. And it's about freeing up their time to focus on the complex, important work that really matters.

While developer frameworks offer endless flexibility and big enterprise systems promise massive scale, a platform like eesel AI gives most support teams the best of both worlds: powerful, customizable AI in a package you can actually set up yourself in minutes.

Stop letting your team waste time searching for answers they should have at their fingertips. Unify your knowledge, automate the repetitive work, and see what your team can really do.

Start your free eesel AI trial today.