Choosing knowledge base software used to be mostly about where you stored documents and how easily people could search them. AI has changed the buying decision.
Your knowledge base may now need to answer questions directly, supply information to AI agents, keep customer service teams working from approved content, spot gaps, and pull useful answers from information spread across different systems.
That creates a much bigger choice for operations and finance teams. Do you need a dedicated AI knowledge base? A company-wide knowledge management system? Customer service software with knowledge features built in? Or enterprise search that works across tools you already use?
We’ve compared seven options worth considering in 2026, focusing on what each one is built to do.
AI knowledge base software compared
| Software | Best suited to | AI and knowledge focus | Pricing approach |
|---|---|---|---|
| Resolvable | Customer service operations using AI and human support | Approved knowledge for AI answers, customer service, and human agents | See current Resolvable AI pricing |
| Guru | Larger organisations needing governed internal knowledge | Verification, permission-aware answers, knowledge maintenance, and enterprise integrations | Custom pricing |
| Slite | Teams wanting an internal knowledge base with automated upkeep | AI search, stale-content detection, suggested updates, and connected knowledge | From $10 per user/month annually |
| Document360 | Customer-facing help centres and product documentation | AI-assisted authoring, search, answers, analytics, and documentation management | Custom pricing |
| Notion | Businesses combining documentation, collaboration, and internal search | Workspace search plus connected-app search and cited AI answers | AI search included with qualifying plans |
| Zendesk | Customer service teams already using the Zendesk ecosystem | Help centre content, self-service, support workflows, and AI agents | Depends on Zendesk plan and AI usage |
| Glean | Large organisations with knowledge spread across many applications | Enterprise search across company systems with permission-aware answers | Custom enterprise pricing |
The right choice depends heavily on where your information sits today and what you want the system to do with it.
Learn more: AI knowledge base: The complete guide for 2026
1. Resolvable
Best for: Customer service teams that want approved knowledge to support both AI and people
Resolvable takes a customer service-specific approach to the AI knowledge base.
Rather than treating the knowledge base as another company wiki, it gives AI systems an approved body of information they can use when answering customer questions or completing customer service work. This puts accuracy, ownership, testing and upkeep at the centre of the setup.
The approach becomes useful when AI is customer-facing. A well-written internal document isn’t automatically good source material for an AI agent, as information can be outdated, contradictory, too vague, or buried in formats that make reliable retrieval difficult.
Resolvable’s approach is designed around giving AI access to information it can interpret and retrieve when a real question comes in. Source visibility and controls also help teams see where answers came from and identify gaps or conflicting information.
It fits particularly well if your goal is customer service automation rather than just company-wide document management.
Resolvable can also sit within a broader service model. Resolvable AI handles repeat customer queries, while human agents (either your own in-house agents or outsourced agents from Resolvable Outsourcing) remain available for work that needs human judgement or a more nuanced response.
Consider Resolvable if you need:
- A knowledge base specifically designed around customer service and AI use
- Approved source information rather than unrestricted document search
- AI answers that can be traced back to their source
- Ways to identify missing or conflicting knowledge
- A setup that can support both AI and human customer service
2. Guru
Best for: Larger organisations that need tighter governance over internal knowledge
Guru has moved beyond the traditional company wiki model towards what it describes as an AI knowledge layer.
Its focus is heavily weighted towards keeping company information trustworthy. Guru combines AI search with verification workflows, usage signals, permission controls, integrations and Knowledge Agents that can work with company content.
One area where Guru stands apart is knowledge verification. Teams can assign responsibility for content, while its AI tools help review information and flag material that may need attention. Guru also supports cited, permission-aware answers rather than allowing every employee or AI system to access every source indiscriminately. This makes it a stronger fit for large businesses where knowledge governance is as important as retrieval.
Pricing is customised around factors including organisational scale, knowledge complexity and AI use rather than a simple public per-user tier. Guru also uses credits to measure AI work such as searches, generated answers and automated Knowledge Agent tasks. For finance teams, that means modelling expected AI usage as well as the base platform cost.
3. Slite
Best for: Internal teams that want their knowledge base to need less manual upkeep
Slite combines a relatively traditional internal documentation system with increasingly active AI maintenance.
Its Ask feature lets employees ask questions in everyday language and receive answers based on workspace documents, with the source material shown alongside the response. On higher plans, Slite Agent can also search connected tools and help find information that’s no longer current.
The Pro plan goes further by detecting differences between documentation and information in connected systems, suggesting changes, and sending them through human approval rather than automatically rewriting company documentation. This makes Slite interesting for operations teams dealing with a common knowledge-management problem: creating documentation is easy enough, but keeping hundreds or thousands of pages current is not.
Public pricing also makes initial cost comparisons easier. Slite currently lists Basic at $10 per user/month and Pro at $20 per user/month when billed annually, with Enterprise pricing available separately.
Slite is primarily an internal knowledge product, though, so businesses looking specifically for customer-service automation may need other software alongside it.
4. Document360
Best for: Customer help centres, product documentation and structured external knowledge
Document360 is one of the more established dedicated knowledge base platforms in this comparison.
It supports internal knowledge, public help centres, product documentation, standard operating procedures, API documentation and other structured content.
Its Eddy AI tools add AI writing, content and FAQ creation, search and answer functionality, glossary creation and duplicate-content detection. Document360’s Ask Eddy AI feature can respond directly to questions using published knowledge base content and provide citations rather than simply returning a list of search results.
Document360 has also been adding more analytics around AI queries. Teams can review which questions Eddy answers and which go unanswered, which can help uncover gaps in documentation. This makes it a strong option when producing, publishing and maintaining documentation is the central requirement.
Pricing is quote-based, and factors such as the number of workspaces, languages, team accounts, security needs, privacy requirements and AI use can affect the final price. For CFOs comparing platforms, this makes it worth asking for a realistic quote based on your planned scale.
5. Notion
Best for: Businesses that want documentation, collaboration and AI search in the same workspace
Notion is broader than dedicated knowledge base software. Many companies already use it for project work, internal documentation, databases, meeting notes and team wikis, and that breadth can be an advantage if the information you want AI to use already lives there.
Notion Enterprise Search can answer questions from the Notion workspace and connected applications. Supported connections include tools such as Slack, Google Drive, Jira, GitHub, Microsoft Teams, SharePoint and OneDrive. Answers include source citations, while access to connected information follows user permissions. For operations teams, this can reduce the need to move everything into a new central repository before making it searchable.
The trade-off is focus. Notion is a broad workspace rather than a dedicated customer-service knowledge system, so you may still need separate support software, customer-facing automation or more specialised governance processes.
It makes most sense when reducing software sprawl and improving internal access to information are bigger priorities than building a dedicated customer support knowledge layer.
6. Zendesk
Best for: Customer service organisations already using Zendesk
Zendesk is another case where the knowledge base is part of a much larger customer service platform.
Zendesk Knowledge can support public self-service content as well as internal knowledge, permissions, agent article creation and other help-centre functions. Its wider Suite includes ticketing, messaging, voice, chat, reporting and agent workspace features.
Zendesk also connects knowledge with its AI agent offering. AI agents can use help-centre information when responding to customer questions, making Zendesk particularly relevant if your support operation already runs there.
The commercial structure needs closer inspection than a simple per-seat comparison. Zendesk has different Suite plans and AI packages, with some AI features included and other functionality or usage charged separately. AI agent pricing can also depend on automated resolutions.
If you already use Zendesk, consolidating knowledge and customer service within the same ecosystem may reduce implementation work. If you don’t, buying an entire support platform primarily for its knowledge capabilities may be harder to justify.
7. Glean
Best for: Enterprise-wide search across many existing systems
Glean solves a somewhat different problem from a conventional knowledge management system.
Instead of asking teams to move their information into one new repository, Glean searches across existing business applications. Its enterprise search product connects company information and can return direct answers based on content held across those systems.
Glean says it supports more than 275 app connectors and applies existing source permissions when returning results. It also uses company context and a knowledge graph to make results more relevant to each user. This approach can make sense for a large organisation with information spread across Microsoft 365, Google Workspace, Slack, Salesforce, Jira and other systems where centralising everything would create a major migration project.
But Glean is closer to enterprise search and enterprise AI infrastructure than a straightforward knowledge base. Pricing is custom, and buyers should account for more than software licensing. Data preparation, integrations, usage patterns, indexing requirements, security needs and rollout work can all affect the overall economics of enterprise AI search.
How to choose the best knowledge base software for your business
There isn’t one best knowledge base software choice for every organisation. Start with the problem you need to fix.
A few questions will narrow the field:
- Who needs the answers? Employees, customer service agents, customers, AI agents, or several of these groups?
- Where does your information live now? A single help centre is very different from information spread across dozens of applications.
- How controlled do answers need to be? Customer-facing or regulated use cases may require stronger ownership, permissions, source controls and review processes.
- What happens after an answer is found? Some products stop at search. Others connect knowledge to customer conversations, workflows or automated actions.
- What will usage cost at scale? Look beyond the advertised licence price and check AI credits, automated-resolution fees, add-ons, implementation costs and future user growth.
- Who owns the knowledge after launch? Someone still needs responsibility for approving changes, reviewing gaps and removing outdated information.
It’s easy to underestimate the last point. AI can make finding and using company knowledge much faster, but a knowledge management system still depends on the quality of the information underneath it. If two policies contradict each other, faster retrieval does not solve the problem.
Dedicated AI knowledge base or broader knowledge management software?
A broad knowledge management software platform is useful when the problem is internal access to information. Your staff may need a company wiki, document management, enterprise search, or easier access to information spread across different departments.
A dedicated AI knowledge base becomes more relevant when AI systems themselves need dependable source information.
For customer service, that distinction is particularly important. AI responses can go directly to customers, so incorrect or outdated knowledge quickly becomes a customer service problem rather than an internal productivity inconvenience.
If you’re evaluating AI for customer service, it’s worth testing the knowledge layer with the same questions customers typically ask. This includes vague questions, follow-ups, exceptions and cases where the AI should hand over to a person.
Look at more than the feature list
Most leading platforms now offer some combination of natural-language search, generated answers, integrations, content creation or AI assistance. That makes feature checklists less useful than they once were.
Give shortlisted suppliers a representative set of documents and real questions. See whether the software finds the right answer, cites the right source, respects permissions, recognises when information is missing, and handles contradictions safely. Then look at the commercial side.
A cheaper platform can become expensive if your team spends months cleaning up content manually. A more expensive system may be difficult to justify if you only need a simple internal wiki with better search.
The right choice is the one that solves the knowledge problem you have. If your priority is making customer service knowledge usable by AI as well as people, explore Resolvable’s AI knowledge base. It’s designed around approved business information that AI can retrieve when answering customer queries, while giving your team greater control over where those answers come from and where knowledge still needs work.
You can also explore Resolvable AI if you want AI to handle repeat customer queries using that knowledge while keeping human agents available for work that needs them. Or, if you’re ready to look at your current setup and where an AI knowledge base could fit, book a demo.
Frequently asked questions
What is AI knowledge base software?
AI knowledge base software stores or connects approved information and uses AI to help people or other systems retrieve useful answers. Depending on the product, it may also generate content, identify outdated information, cite source material, search connected applications, or feed information into customer service AI agents.
What is the difference between a knowledge base and a knowledge management system?
A knowledge base is usually a structured collection of information that people or systems can search and use. A knowledge management system can cover a wider set of processes, including creating, reviewing, organising, governing, sharing and maintaining knowledge across an organisation.
Can AI knowledge base software replace a traditional company wiki?
Sometimes. If your main requirement is internal documentation and search, modern AI knowledge base software may cover both needs. Larger organisations may still need more detailed publishing, permissions, workflow, audit or document-management features, so replacing an existing wiki should be tested against those requirements.
How should a CFO compare AI knowledge base software costs?
Compare total operating cost rather than licence prices alone. Check user fees, AI usage charges, additional modules, implementation work, integrations, migration, support and the internal time required to keep content accurate. Pricing models vary significantly between vendors, so a realistic usage scenario gives a better comparison.
