Top AI search tools for company knowledge: Docs, wikis and code

It is reasonable to use CodeAlive if engineers need to see how the behavior is distributed across files, dependencies, tests, and services.

CodeAlive utilizes semantic search, keyword search, and code relationships graph. CodeAlive's Deep Research tool responds to questions between repositories and provides a report with references to the implementation. Coding assistants and agents can provide context via API and MCP connections too.

That means CodeAlive would be useful when onboarding or investigating an issue and finding a function is not enough. The investigation of the payment flow, for instance, will require following API requests through multiple services and understanding which tests are covering each step.

While evaluating the tool, try to ask a question that goes beyond the bounds of service. Check if CodeAlive finds a call chain, the right files, and distinguish observation from assumption about the system.

Glean offers enterprise search across applications within a company. Glean makes it easier for employees to find information irrespective of where it is housed without changing permissions on the data sources.

This comes in handy especially when the knowledge is scattered across various departments within the company. An answer to a problem could involve accessing a plan, conversation, and the decision made elsewhere.

Glean would be a good choice especially when that fragmentation affects the entire company. Glean does not only offer a search tool but a search experience across sources.

Test questions to ask: Questions that involve information from various applications. Relevant results and source freshness. Different user permissions results.

Guru becomes useful when there is available information, but it is hard to determine which guidance is valid and up-to-date.

Guru unites AI-based search with workflows of ownership and verification. While responses from Guru contain sources, the verification features allow handling the status of the knowledge itself.

The difference is important for the evolving procedures. An employee in the support team who has found the outdated refund policy will need not only the relevant excerpt but also the valid one and confirmation of its validity.

Guru becomes a good tool for evaluation if used for checking the content managed by teams working on the operational guidance and internal policies with defined owners. Nevertheless, the software will not be able to solve all policy-related issues. Someone still will have to be responsible for the review and correction of the material.

Evaluate Guru with the example of the outdated procedure and the correct one. Consider the response, its citations, and verification status.

Notion Enterprise Search becomes a logical first step where Notion already occupies a considerable market share for company documentation.

Notion Enterprise Search gathers data from the workspace and linked apps, such as Slack, Google Drive, and Jira. Company-based answers feature references, while users may limit their queries to certain sources or workspaces.

It gives a straightforward solution to testing more complex search without involving an additional tool for employee search.

Source selection requires consideration. Notion search can gather information from the web, and hence an internal policy question needs to be limited to company-related information only.

Notion Enterprise Search is a much less intuitive initial step where the company does not utilize Notion very much. Connector coverage needs to be verified first.

Perform a test using two sources, a Notion project page and a connected one. Make sure that both sources contribute to the answer and that updates are taken into account.

Atlassian Rovo can be considered in cases where the project information is distributed among Jira issues, Confluence pages, and other workplace applications connected to them.

Rovo offers search, chat, and agents functionality with connectors providing information beyond Atlassian solutions. The applicability of Rovo is at its peak if your company is already using Jira and Confluence for keeping track of work and decisions.

The example of a good question would be related to the history of the project. It can involve a Confluence page describing the first plan and subsequent activities in Jira reflecting some change. Thus, the response should not just copy the source text but should reflect the timeline.

Rovo will cover as many sources as it has connectors. Make sure that you examine the set of supported content and access methods for each of the workplace applications which you plan to use.

For teams whose major task is to trace implementation details across the code repositories, you should also consider specialized code search solutions. While project documentation tells about intentions, code reveals what the system actually does.

Deep Search from Sourcegraph will be pertinent if the software developer needs AI-based investigation as well as exact code searching.

In other words, the product integrates an agent for code research and search features allowing exact matching and regular expressions. This means that engineers will have several options for investigating the same system in terms of locating an existing identifier and looking through its context.

As an example, engineers may look up an error message and trace the ways of its emergence in services. The assessment will evaluate not only the found code but also the explanation, as even a valid one can fail to take into account an important branching point.

Sourcegraph will be considered for engineering purposes if code searching is an ongoing activity. It does not substitute the general knowledge base in case of company policies or business documents.

Consider the relevance, completeness, and ease of code inspection regarding the exact search and the architectural issue whose solution is known to your engineers.

Onyx should be taken into account if deployment control is necessary and your organization is capable of running the search infrastructure.

Onyx provides open-source enterprise search and an AI assistant, with self-hosting possible and multiple model choices. Among the connected sources, you have office applications like Slack, Confluence, SharePoint, Google Drive, and Jira.

Your choice is based not only on where your software will be deployed. The self-hosted solution requires maintenance of connectors, indexing, access control, monitoring, and updates.

Inquire about the features that are supported by the version you are going to deploy. Check the permission handling and model integration instead of assuming all the capabilities are included in every deployment.

Onyx is not the easiest solution for a team that needs a service requiring minimal maintenance effort.

While testing the service, you need to assess its performance and maintenance effort. Include document modification, document deletion, and permission changes in the test set.

Slite is a good option when the team requires maintaining a knowledge base and providing responses using the documentation.

The Slite Ask tool provides answers along with source documents. The documentation verification process of Slite prioritizes verified information and puts outdated information at the bottom.

There are significant differences in the available plans. The documentation of Slite distinguishes the Ask tool available in the Basic plan with the Slite Agent in the Pro and Enterprise plans where the search covers the connected applications.

Slite can pay a valuable role in onboarding support, periodic processes, and team documentation. The evaluation of Slite can include checking if new hires get the answers even if they do not know the folder structure of the wiki.

When the main need is the code analysis process, Slite is less suitable. When the information is scattered across different applications, the teams should verify the coverage of connectors in the plan.

The documentation gap is important to identify during the trial. It needs to be checked whether Slite highlights the documentation gap rather than giving an unsupported response.