A knowledge catalog makes company knowledge discoverable, understandable, verifiable, and controllable for AI agents. That's precisely the point: not every AI should be allowed to read everything, but rather to use the right information in the right business context—with clear access rules, accountability, data quality, and human oversight.
I see the same pattern time and again with SMEs: Enthusiasm for AI is high, but the underlying knowledge is scattered. Offers are buried in old folders, product details in Word documents, website copy in WordPress, customer data in CRM systems, project information in emails, decisions in chat histories, and key performance indicators in Excel or BigQuery. For humans, this chaos is cumbersome. For AI agents, this chaos is a risk.
A Knowledge Catalog Therefore, it's not solely an enterprise issue. For small and medium-sized enterprises (SMEs), a knowledge catalog is a pragmatic organizational system: It explains what information exists, what that information means, which sources are trustworthy, who is responsible, and what an AI system is allowed to do with it. This is practical knowledge management for companies that want to not only test AI but also use it responsibly.
AI is not a shortcut. AI amplifies what is already clear, cultivated, and accounted for within the company—or the chaos that already exists.
If you want to use AI agents effectively, you don't need the biggest tool first. You first need clarity about your own knowledge. This is precisely where AI readiness begins: not with the software, but with the question of whether your company knows what it knows.
Why a Knowledge Catalog is becoming important for AI agents
A AI agent It's more than just a chat window. An AI agent can track goals, plan tasks, use tools, retrieve information, and independently prepare or execute individual process steps. The more an AI agent is allowed to do, the more important the data context becomes.
Raw data access only answers one technical question: "Where is the data located?" A knowledge catalog answers the more business-related questions:
- What does this information mean? "Revenue" can mean gross, net, paid, booked, forecasted or cancelled.
- Which source is authoritative? The website, the CRM, the accounting, or the latest quote template?
- How up-to-date is the information? An outdated pricing component can make an offer incorrect, even if the AI is technically sound.
- Who is responsible? Without clear responsibility, no one can correct incorrect definitions, duplicate files, or contradictory statements.
- What uses are permitted? An internal memo may be analyzed, but it may not be included in an external customer email.
- Which data is sensitive? Customer data, employee data, margins, complaints and contracts require special protection rules.
Without this clarification, not only technical errors occur. Incorrect offers, inconsistent communication, poor reports, data protection risks, and automated decisions for which no one is properly accountable will result.
The real risk: AI accelerates false knowledge
Many companies first ask: "How can we use AI faster?" I usually ask in return: "What false, old, or contradictory information would AI then use faster?"
Here are a few typical examples from SME projects:
- Incorrect definition of revenue: The sales team talks about offer value, the accounting department about paid invoices, and management about planned annual revenue. An AI agent then generates a report that sounds plausible but mixes three different logics.
- Outdated offer components: A folder contains five versions of the same service description. The AI finds the old version with an outdated price and uses it to create a new offer.
- Inconsistent brand knowledge: The website uses different language than the sales materials, social media uses different terminology, and internal presentations contain outdated positioning. The AI then writes something that is formally appropriate, but not brand-consistent.
- Sensitive customer data: An agent summarizes project information and includes internal remarks that should never be included in customer communications.
- Support without an approval process: An AI system answers queries with outdated warranty terms because no one has defined the valid version as the single source of truth.
The problem is not that AI is inherently unsuitable. The problem is that an AI agent without a business context cannot know which information is binding, current, permissible, or risky.
Data Catalog, Knowledge Catalog and Owned Knowledge explained simply
A data catalog It primarily describes data sets: tables, columns, files, systems, technical metadata, and origin. This is important, but often insufficient for AI agents.
A Knowledge Catalog It goes further. A knowledge catalog connects data sources with their meaning, business context, responsibility, data quality, approval process, and permitted use. A technical data list becomes an understandable knowledge map for humans and AI systems.
The difference can be described practically as follows:
- Data Catalog: "This table exists, these columns are present, this file is located there."
- Knowledge Catalog: "This source is authoritative, this metric means exactly this, this person is responsible, this data may be used for internal analysis, but not for external communication."
For SMEs, another strategic concept comes into play: Owned KnowledgeOwned knowledge means that your important company knowledge isn't just stored on platforms, chat histories, folders, or in the minds of individuals. Your knowledge is structured, exportable, maintainable, and usable by both people and machines.
Many businesses have understood why Owned media is more important than rented reach.The same logic now applies to knowledge: If your company doesn't structure its own knowledge, platforms, tools, and individuals become a dependency.
What should be included in a knowledge catalog?
A knowledge catalog doesn't have to contain everything. A good knowledge catalog primarily contains the knowledge that is important for decision-making, communication, and recurring work.
For an SME, these building blocks are usually sufficient to get started:
- References: Website, CRM, offer templates, product data, project management, accounting, cloud folders, internal documentation, support knowledge and analysis tools.
- Metadata: Source name, description, system, format, recency, owner, sensitivity, and release status.
- Definitions of terms: Clear explanations for lead, customer, order, revenue, margin, project status, conversion, maintenance, warranty or regular customer.
- Single Source of Truth: A clear definition of which source applies in case of discrepancies.
- Responsible: People who can professionally decide whether information is correct.
- Access rules: who is allowed to read, modify, export, analyze, or allow AI access.
- Data quality: Information regarding timeliness, completeness, duplicates, errors, uncertainty and release.
- Relationships: Links between products, target groups, offers, processes, campaigns, key figures and responsible parties.
- Test questions: Typical questions that a human or AI agent must be able to answer correctly.
- Permitted AI use: clear rules on whether an agent may only read, summarize, analyze, suggest, execute, or externally communicate information.
The last point is crucial. An AI agent that summarizes internal documents poses a different risk than an AI agent that automatically generates offers, modifies customer data, or sends emails.
What is an AI agent allowed to do with which source?
I recommend a simple permissions logic for small teams. Not every source needs the same protection, but every important source needs a decision.
- Read: The AI agent can retrieve information, but cannot modify it. Suitable for shared knowledge articles, public website content, or internal process descriptions.
- Summarize: The AI agent is allowed to condense content, such as project status or meeting notes. Sensitive statements must be reviewed.
- Analyze: The AI agent can recognize patterns, compare key figures, or provide error messages. This requires defined terms and data quality.
- Suggest: The AI agent can prepare offer components, answers, or next steps. A human approves the result.
- Carry out: The AI agent is allowed to initiate actions, such as creating tickets or updating data records. This requires strict rules, logging, and clear accountability.
- Communicate externally: The AI agent is allowed to send content externally. In SMEs, this should only happen with very clear boundaries and human oversight.
This distinction seems simple, but prevents many problems. Because it forces you to differentiate between "AI is allowed to help me" and "AI is allowed to act for me".
Examples: Where a Knowledge Catalog specifically helps in the everyday life of SMEs
Offer preparation
When it comes to proposals, a lack of clarity becomes immediately apparent. What services are currently offered? Which wording has been approved? What assumptions apply? Which references are suitable? Which discounts are permitted?
A knowledge catalog can organize service descriptions, pricing logics, offer components, references, target groups, and internal rules in such a way that an AI system can perform meaningful preliminary work. The agent then doesn't just create any offer, but works with approved knowledge. If you waste time right here, the contribution to... AI-supported offer creation a good in-depth analysis.
Internal search and onboarding
New team members often ask the same questions: Where is everything located? Who decides what? How does a project transition work? What terminology do we use? Which tools are mandatory?
A knowledge catalog enables semantic search. Semantic search means the system searches not just for exact words, but for meaning. If someone asks, "How do we hand over a website project?", the system should find the project workflow, checklist, responsible parties, templates, and approval guidelines.
Website and brand knowledge
As a branding strategist, this area is particularly important to me. A brand consists of more than just a logo and colors. A brand consists of meaning, language, attitude, understanding of the target audience, and recurring decisions.
When your brand knowledge is clearly structured, AI can write more consistently, check content, and detect inconsistencies. This includes tone of voice, value propositions, prohibited statements, evidence, target groups, terminology, and positioning. In our work surrounding Branding and positioning Therefore, it always becomes clear: AI can support brand work, but it must not replace attitude.
Reporting and key performance indicators
Reporting often appears objective, but in many SMEs it's riddled with interpretations. What constitutes an active customer? When is a deal considered won? Which costs should be included in the margin? Which campaign should be credited with the conversion?
A knowledge catalog defines these terms. This allows an AI agent to better explain reports, classify anomalies, and answer questions more naturally. For example, Google describes features for Gemini in BigQuery such as natural language for SQL generation and Data Canvas for exploring data assets. Gemini in Looker allows queries to data sources in natural language. However, it's important to remember that natural language doesn't improve poor definitions.
Support and customer communication
An AI agent in support can answer recurring questions more quickly. But only if warranty conditions, performance limits, up-to-date product information, and escalation rules are maintained.
Without a knowledge catalog, support automation can damage trust. A knowledge catalog allows an agent to recognize: This answer is approved, this information is outdated, this request contains sensitive data, this case needs to be escalated to a human.
Project handovers
In small teams, project knowledge often resides in the minds of individuals. When someone is sick, leaves the team, or is overloaded, gaps arise. A knowledge catalog helps to streamline handovers: project goals, status, open decisions, risks, contact persons, files, next steps, and approvals become easily accessible.
MCP explained simply: controlled bridge instead of tool hype
The Model Context Protocol, short MCPMCP is a standardized way to connect AI applications to external data sources and tools. Anthropic announced MCP on November 25, 2024, as an open standard for secure two-way connections between data sources and AI-powered tools.
For you as an entrepreneur, the technical architecture isn't the crucial factor. What matters is the logic: MCP can act as a controlled bridge between AI agents, tools, and shared knowledge. An agent then doesn't access everything indiscriminately, but rather uses defined interfaces with clear rules.
Google Cloud documents that the former Dataplex Universal Catalog has been discontinued since April 10, 2026. Knowledge Catalog It is called MCP and is intended to provide business context, governance, and a context graph for AI agents. Google also describes Knowledge Catalog integrations with MCP, Gemini CLI, and MCP Toolbox. For SMEs, the underlying logic is particularly relevant: MCP and Knowledge Catalog can work together to help connect AI agents to shared company knowledge in a controlled manner.
This is a current product example, but not the only possible implementation. An SME can start with Microsoft systems, WordPress, CRM, local files, Notion, a wiki, or its own data structure. The central question always remains the same: What knowledge is shared, understandable, up-to-date, and who is responsible for it?
Data governance is not a bureaucratic monster
Many small businesses react skeptically to Data GovernanceUnderstandable. Nobody needs additional bureaucracy. But good data governance doesn't mean more paperwork. Good data governance means less risk, less search time, and fewer wrong decisions.
For SMEs, data governance primarily means:
- clear responsibilities instead of “someone probably knows this”,
- released sources instead of files with names like "final_final_neu",
- understandable terms instead of internal abbreviations that only two people understand,
- verified access instead of blindly trusting every new tool,
- regular care instead of a one-off cleanup campaign,
- human control where an answer becomes legally, financially, or humanly relevant.
This is not optional, especially when it comes to personal data. According to Article 5(1) of the GDPR, personal data must be processed for specific purposes, limited to what is necessary, and protected by appropriate technical and organizational measures. For AI agents, this means that not everything that is technically readable can be processed.
What data should not be fed into AI systems without verification?
A knowledge catalog isn't meant to unlock everything. It's meant to help you make informed decisions. You should be especially careful with the following data:
- Personal customer data: Names, email addresses, phone numbers, addresses, purchase histories, complaints, or health information.
- Employee data: Contracts, salaries, performance reviews, sickness data, or internal conflicts.
- Financial data: Margins, liquidity, internal calculations, unpublished sales or bank data.
- Contract information: Special conditions, legal obligations, confidentiality clauses, or ongoing negotiations.
- Strategic information: Pricing strategy, new products, internal weaknesses, acquisition plans or competitive analyses.
- Unverified notes: Raw transcripts, private comments, unapproved assessments, or emotional interim reports.
This data may be useful in certain cases. But not without a purpose, access control concept, technical safeguards, logging, and explicit authorization.
The minimal version for small teams
You don't have to start with a large platform. For many small teams, a simple, minimal version that is consistently maintained is sufficient.
A lean starter version might look like this:
- a central document including the most important sources, responsible parties and rules,
- a wiki or notion structure for terms, processes and approvals,
- a defined folder with approved offer modules and current service descriptions,
- a CMS area for brand knowledge, service pages, target groups and evidence,
- a simple list of sources Status: released, outdated, unsafe, sensitive, not for AI use.
That sounds unspectacular. That's precisely why it works. Small businesses don't need a cumbersome system at the beginning, but rather a reliable location and clear support.
30- to 90-day plan: How to get started pragmatically
A knowledge catalog isn't implemented through a tool, but through decisions. I would divide the implementation into three phases.
Phase 1: The first 30 days — clarifying the use case and sources
- Choose a specific use case: Proposal preparation, internal search, reporting, support or onboarding.
- Formulate the goal: Which tasks should become easier, safer, or faster?
- List the most important sources: A maximum of 10 to 20 sources for the first pass.
- Mark sensitive sources: Customer data, employee data, financial data and confidential documents.
- Determine the Single Source of Truth: Which source is considered authoritative when information is contradictory?
- Collect real questions: What questions does your team keep asking today?
You don't have to be finished after 30 days. You should know which area needs to be organized first and which sources will be used for that.
Phase 2: Days 31 to 60 — Defining terms, quality and responsibility
- Define key terms: Write down what revenue, lead, project, customer, order, margin, or status mean.
- Designate responsible parties: Every important source needs a specialist contact person.
- Evaluate data quality: current, outdated, incomplete, duplicate, contradictory or verified.
- Define access rules: Who is allowed to read, modify, export, or have AI access?
- Build a simple approval process: Who reviews new content before an AI agent is allowed to use it?
- Remove or block contaminated sites: Outdated price lists and invalid templates are riskier than missing content.
After 60 days, your first knowledge catalog area should be clear enough for a new person on the team to understand the logic.
Phase 3: Days 61 to 90 — Limit and test AI access
- Create test questions: Use real questions from offers, support, reporting, or onboarding.
- Test answers against expert knowledge: Are the sources, definitions, and conclusions correct?
- Limit AI rights: Read, summarize and suggest at the beginning — do not execute automatically.
- Log errors: Each incorrect answer shows you which source, definition, or rule is missing.
- Establish a care routine: monthly for operational content, quarterly for terms and rules.
- Decide on the next use case: Only when one area is stable can the next one be addressed.
If you want to assess your maturity level beforehand, a AI Readiness Check for SMEs AI readiness doesn't mean everything has to be perfect. AI readiness means that risks, data, processes, and responsibilities are consciously clarified.
Checklist: Is your company ready for its first Knowledge Catalog?
If you find yourself nodding in agreement to several points, the time is right:
- Your team regularly searches for the same information.
- There are several versions of offer templates, price lists, or service descriptions.
- Important process information resides in individual minds.
- Key performance indicators are interpreted differently.
- CRM, website, accounting, project management, and cloud folders each contain a part of the truth.
- You want to use AI agents for quotes, support, reporting, or internal search.
- There is sensitive customer data or confidential project data.
- No one can spontaneously say which source is authoritative.
- New team members need a long time to understand internal processes.
- You want to use AI without outsourcing responsibility to a tool.
When is a knowledge catalog good enough for the first AI agent?
A knowledge catalog is good enough for the first AI agent if five conditions are met:
- The use case is clear: The agent has a limited task, not a general-purpose role.
- The sources are released: The agent only uses verified or deliberately marked information.
- The terms are defined: Key performance indicators and technical terms are clearly described.
- Rights are limited: The agent is allowed to prepare rather than execute at the beginning.
- The human remains in the release process: Critical results are reviewed before they have an impact externally or within systems.
This is where AI becomes practical. Not perfect. But controlled, capable of learning, and responsible.
Why this is also a brand issue
A knowledge catalog might initially sound like data management. In reality, it's also about brand building. Because every automated response, every offer, every support text, and every report shapes how your company operates and is perceived.
If your company's knowledge is unclear, your AI will also communicate unclearly. If your brand is clearly positioned, your website is comprehensibly structured, and your processes are transparent, AI can enhance this clarity.
That's why at Berger+Team we don't think about branding, website, marketing, automation, and AI separately. In our work surrounding AI and Digitalization It's not about installing a tool. It's about creating a system that reduces your team's workload and enables better decisions.
My conclusion: AI needs clarity, not just access.
A knowledge catalog is the bridge between company knowledge and reliable AI agents. Its true value lies not in the catalog itself, but in the clarity it creates: Which data is relevant? What does this data mean in the business context? Who is responsible? What is an agent allowed to do? What requires human approval?
This presents a real opportunity for SMEs. Small businesses can make faster decisions, structure more pragmatically, and work more closely with their own expertise than large organizations. But only if AI isn't misunderstood as a shortcut.
My advice: Build your owned knowledge first. Start small, but with commitment. Choose a use case, clarify sources, define terms, assign responsibilities, limit AI access, and review results humanly. Then AI won't become an uncontrolled experiment, but a tool that enhances clarity.
FAQ: Knowledge Catalog, AI Agents and Corporate Knowledge
What is a Knowledge Catalog?
A knowledge catalog is a structured knowledge and metadata map of your company. It describes what data, content, and processes exist, what they mean, who is responsible, their quality, and how humans or AI agents are allowed to use them.
What is the difference between a knowledge catalog and a data catalog?
A data catalog primarily displays technical data such as tables, files, columns, and systems. A knowledge catalog complements this view with business context, definitions of terms, responsibilities, data quality, approval processes, and permitted AI use.
Why do AI agents need a business context?
AI agents need business context to properly interpret information. Without context, an agent might use outdated pricing, incorrect revenue definitions, or sensitive notes, generating seemingly plausible but risky results.
How small can an SME start?
An SME can start very small: with a use case, a list of resources, a glossary of key terms, and clearly defined responsibilities. To begin with, a wiki, a structured document, or a shared folder is often sufficient, provided that maintenance and access rules are enforced.
What does it cost to get started with a Knowledge Catalog?
Getting started depends less on the tool itself than on the scope of the project. If you begin with a clear use case, the greatest effort usually comes from reviewing, cleaning up, defining, and approving – not from the software itself.
What tools do I need for a knowledge catalog?
You don't necessarily need a specialized enterprise tool to get started. A wiki, Notion, SharePoint, a clean folder system, a CMS area, or an internal document can suffice; what's important is structure, accountability, up-to-dateness, and clear rules for AI access.
Who should be responsible within the company?
The responsibility should not lie solely with IT. Subject matter experts must decide whether content is correct, while management or project leaders define rules for access, sharing, and usage.
Which data should not be used in AI systems without verification?
Personal data, employee data, financial data, contract information, internal strategies, and unverified notes must not be fed into AI systems without scrutiny. This data requires purpose limitation, data minimization, access control, and a clear decision as to whether an AI agent is even permitted to process it.
What does MCP have to do with a Knowledge Catalog?
MCP stands for Model Context Protocol and enables AI applications to connect to shared data sources and tools in a controlled manner. In conjunction with a knowledge catalog, MCP helps ensure that an AI agent does not access data arbitrarily, but rather uses defined sources, rules, and boundaries.
How does a knowledge catalog help against AI hallucinations?
A knowledge catalog reduces hallucinations because AI agents access verified sources, defined terms, and approved knowledge. The catalog does not replace human oversight, but it provides the AI system with a solid foundation.
When is an AI agent allowed to act automatically?
An AI agent should only act automatically once sources, rules, responsibilities, tests, and logging are stable. In SMEs, it usually makes sense to start with reading, summarizing, and suggesting information, and then have critical actions approved by humans.
How often does a knowledge catalog need to be maintained?
You should review operational content such as prices, services, and support policies at least monthly. In many SMEs, you can review terminology, access rules, and data quality quarterly, as long as there's a direct approval process for important changes.
Sources
- Anthropic: Introducing the Model Context Protocol — anthropic.com (2024)
- Google Cloud Documentation: Knowledge Catalog overview — docs.cloud.google.com (2026)
- Google Cloud Documentation: Use Knowledge Catalog with MCP, Gemini, and other agents — docs.cloud.google.com (2026)
- Google Cloud Documentation: Gemini in BigQuery overview — docs.cloud.google.com (2026)
- Google Cloud Documentation: Gemini in Looker overview — cloud.google.com (2026)
- Regulation (EU) 2016/679, General Data Protection Regulation, Art. 5 — eur-lex.europa.eu (2016)
