Many businesses have large amounts of useful information stored in reports, policies, manuals, emails, databases, presentations, and shared folders. The challenge is often not creating more information, but finding the right information when it is needed.

Cohere develops artificial intelligence tools that can help organizations search, understand, and use their own approved information. Instead of relying only on the general knowledge built into an AI model, a business can connect the system to its internal documents and data.

This can make AI more relevant to the organization because the answers are based on its own policies, procedures, products, research, and experience.

Why Business Information Can Be Difficult to Use

As an organization grows, its information often becomes scattered across many systems. Employees may need to search through:

• Policy and procedure manuals
• Training materials
• Product documentation
• Customer-service records
• Research reports
• Project files
• Meeting notes
• Shared drives and databases

Even when the correct information exists, employees may not know where it is stored, what keywords to use, or whether a document is still current.

A traditional search system may return documents containing the exact words entered by the user. An AI-supported system can also look for information with a similar meaning, even when different words are used.

Connecting AI to Approved Information

A general-purpose AI model does not automatically know the contents of a company’s private documents. The organization must connect the AI system to information that it has approved for use.

For example, a business could connect an internal assistant to:

• Current workplace policies
• Technical support guides
• Product specifications
• Employee training documents
• Frequently asked questions
• Research and planning reports

When someone asks a question, the system can search those sources, find relevant passages, and use them to prepare a response.

The organization remains responsible for deciding which information is included, who can access it, and how the system should be monitored.

What Is Retrieval-Augmented Generation?

One common way of connecting AI to organizational information is called retrieval-augmented generation, often shortened to RAG.

The process usually involves two main steps:

• Retrieval: The system searches the organization’s approved documents and finds information related to the user’s question.
• Generation: The AI model uses the retrieved information to prepare a clear response.

For example, an employee might ask:

“How much notice do I need to give when requesting vacation time?”

The system could search the current human-resources policy, locate the section about vacation requests, and use that material to answer the question.

This approach is often more reliable than asking an AI model to answer from general knowledge because the response can be based on the organization’s own documents.

How Cohere Embed Helps Find Meaning

Cohere’s Embed models can help a search system identify similarities in meaning.

An embedding is a numerical representation of a piece of text. Text with similar meaning is placed closer together mathematically, even when it does not use the same wording.

For example, a user might search for:

“How do I reset my work account?”

The relevant document might be titled:

“Employee Password Recovery Procedures.”

A basic keyword search might not immediately recognize the connection. A search system using embeddings can understand that account reset and password recovery are closely related concepts.

This allows employees to search using natural language instead of needing to know the exact title or wording of a document.

How Cohere Rerank Improves Results

A search system may initially find many potentially relevant documents. Cohere’s Rerank models can review those results and reorganize them so the most useful ones appear first.

For example, a search for information about parental leave might return:

• A current parental-leave policy
• An old policy that is no longer used
• A meeting note mentioning parental leave
• A general government information page
• An employee benefits overview

A reranking system can evaluate how closely each result answers the question and move the strongest source toward the top.

This can reduce the time employees spend opening documents that contain only a passing reference to the topic.

Creating an Internal Knowledge Assistant

One practical use of Cohere is an internal knowledge assistant. Employees could ask questions in ordinary language and receive answers based on approved workplace information.

An internal assistant might help with questions such as:

• How do I submit an expense claim?
• Where can I find the remote-work policy?
• What steps are required to approve a purchase?
• How do I troubleshoot a particular product?
• Which department is responsible for this request?
• What information must be included in a project report?

The assistant could also provide links or references to the documents used to prepare the answer. This allows employees to review the original source when necessary.

Supporting Customer Service

Businesses can also use their own information to support customer-service teams.

A customer-service assistant could search product manuals, warranty policies, troubleshooting guides, and previous support information while an employee is helping a customer.

This could help staff:

• Find answers more quickly
• Provide more consistent information
• Locate the correct troubleshooting steps
• Identify relevant warranty or return policies
• Summarize complex product information

The AI does not have to replace the customer-service employee. It can act as a search and support tool that helps the employee respond more efficiently.

Helping Employees Learn

An AI system connected to training materials can also support employee learning.

A new employee might ask questions about workplace procedures instead of searching through several long manuals. The system could locate the relevant sections and explain them in simpler language.

It could also help employees:

• Review training topics
• Find examples of completed forms
• Understand technical terms
• Compare procedures
• Locate required safety information

Human training and supervision would still be important, especially for safety, legal, financial, or other high-risk decisions.

Summarizing Large Collections of Information

Organizations often need to review large amounts of text. This might include customer feedback, survey responses, research reports, meeting notes, or support requests.

An AI system can help summarize this information and identify common themes.

For example, a business could analyze customer comments to identify repeated concerns about:

• Delivery times
• Product quality
• Website usability
• Customer support
• Pricing or billing

Employees should still review the source material because summaries can miss important details, unusual cases, or context.

Keeping Information Current

An AI assistant is only as useful as the information it can access. If outdated documents remain in the system, the AI may retrieve old procedures or incorrect policies.

Organizations therefore need a process for:

• Removing outdated documents
• Identifying the official version of each policy
• Updating information regularly
• Recording document dates and owners
• Controlling which sources the system can use

Good information management is an essential part of building a reliable AI system.

Privacy and Access Controls

Businesses may store confidential information about employees, customers, finances, operations, or future plans. Connecting AI to this information requires strong privacy and security controls.

An organization may need to decide:

• Which employees can access each collection of documents
• Whether sensitive information should be excluded
• Where information is stored and processed
• How user activity is recorded
• How long information is retained
• How incorrect or inappropriate answers are reported

Not every employee should automatically have access to every source. The AI system should respect the organization’s existing permissions and confidentiality rules.

The Importance of Source Citations

When an AI system answers a business question, it can be helpful to show where the information came from.

A response might include a link to the relevant policy, report, or manual. This allows the user to verify the answer and read the surrounding information.

Source citations are especially valuable when:

• Policies change frequently
• The answer affects an important decision
• Several documents contain similar information
• The user needs the exact wording
• The AI may have misunderstood the question

An AI-generated response should not automatically be treated as the final authority. The original document remains important.

Human Oversight Is Still Necessary

AI systems can make mistakes, misunderstand questions, retrieve the wrong document, or provide an incomplete answer.

Businesses should establish clear rules about when employees can rely on an AI response and when they must consult a manager, specialist, or official document.

Human review is especially important for:

• Legal decisions
• Medical or health information
• Financial approvals
• Employment decisions
• Safety procedures
• Confidential or sensitive matters

The goal is usually not to remove human judgment. It is to help people find and understand information more efficiently.

Could Smaller Organizations Benefit?

Smaller businesses, nonprofits, libraries, municipalities, educational institutions, and social enterprises may also benefit from AI-supported knowledge systems.

A nonprofit could create an assistant connected to its program information, volunteer guides, funding requirements, and internal policies.

A municipality could help staff search bylaws, planning documents, procedures, and public-service information.

A library could improve searches across local resources, community information, and staff documentation.

The system does not need to include every document an organization owns. A smaller project could begin with one well-organized collection of useful information.

Starting With a Clear Problem

Organizations should begin with a specific problem rather than adopting AI simply because it is new.

A useful starting question might be:

“Which information do our employees repeatedly struggle to find?”

A business could then test an AI system with a limited collection of documents and a small group of users.

A careful pilot project can help the organization evaluate:

• Whether the answers are accurate
• Whether employees find the system useful
• Which documents need improvement
• What privacy controls are required
• How much human review is necessary

Final Thoughts

Cohere can help businesses use their own information by improving search, identifying meaning, ranking relevant documents, and supporting question-answering systems.

Its Embed and Rerank tools can help organizations find the right information, while its language models can turn retrieved material into summaries or conversational answers.

The greatest value does not come from the AI model alone. It also depends on accurate documents, clear access controls, thoughtful system design, and human oversight.

When these pieces work together, enterprise AI can help employees spend less time searching for information and more time using it effectively.

In the next article in this series, we will look at Cohere’s Command, Embed, and Rerank models in more detail.