How to Build a Company Knowledge Base for AI Agents

A general-purpose model knows the internet but nothing about your pricing, your policies, or last quarter's decisions. A company knowledge base fixes that: it grounds your AI agents in your own data so answers are accurate, current, and traceable to a source.

Why a company knowledge base for AI matters

Out of the box, a large language model answers from its training data. Ask it about your refund policy and it will guess, often confidently and often wrong. That guessing has a name, hallucination, and it is the single biggest reason teams hesitate to put AI in front of customers or into internal workflows.

A knowledge base changes the mechanics. Instead of relying on what the model memorised, your AI agents retrieve the relevant passages from your own documents at query time and answer from them. This technique, retrieval-augmented generation (RAG), keeps answers tied to material you control. When a document changes, the answer changes with it, no retraining required. Just as important, a well-built knowledge base lets an agent say "I don't know" when the answer genuinely isn't in your data, instead of inventing one.

What you can put in your knowledge base

The goal is to capture the documents your team already relies on and make them searchable by your agents. On AgentWorks you can build a knowledge base from two kinds of sources.

Uploaded files. Drop in PDF, DOCX, TXT, and CSV files directly, product manuals, policy documents, spreadsheets of FAQs, onboarding guides, or exported reports. These are ingested, chunked, and indexed for retrieval.

Connected sources. Rather than exporting and re-uploading, connect the tools where your knowledge already lives. AgentWorks connects to public and internal URLs, Notion, and Confluence, alongside a wider set of integrations including Google Drive, OneDrive, and SharePoint. That means a wiki page or a policy doc stays where your team maintains it, and your agents read from the same source of truth.

Every plan includes a personal knowledge base. On Pro you also get an organisation-level knowledge base, and Team plans add a shared knowledge base so a whole workspace draws on the same grounded content. You can compare what each tier includes on the pricing page.

How retrieval and citations actually work

When you upload or connect a source, AgentWorks splits the content into passages and stores them as vector embeddings using pgvector, a mature, open PostgreSQL extension for similarity search. When someone asks a question, the platform finds the passages most relevant to that question and hands them to the model as grounding context.

The result is an answer built from your material, with citations pointing back to the source passages. This matters for trust: a reviewer can click through and verify that the agent's claim actually appears in the document, rather than taking the model's word for it. If nothing relevant is found, the agent tells you it doesn't know rather than filling the gap with a plausible-sounding guess. You can read more about the approach on the knowledge & RAG page.

Because retrieval happens live, you are never locked to one model's knowledge. In multi-LLM chat you can switch between GPT-5, Claude, Gemini, or Mistral mid-conversation, and each one answers from the same grounded knowledge base. Gemini's context window reaches up to 1M tokens, useful when a single query needs to reason across a lot of retrieved material. See the full list on the models page.

Grounding multi-agent workflows in your data

A knowledge base is most powerful when it feeds more than a single chat. AgentWorks lets you build multi-agent pipelines, for example research, then draft, then review, then publish, where each step can draw on the same company knowledge. A research agent pulls the relevant internal context; a drafting agent writes against it; a review agent checks the output back against the source.

These pipelines can run on a schedule, daily, weekly, or monthly on Pro and above, or fire from a webhook when an external system triggers them. Every step is logged with a per-step risk classification, so you always have a record of which knowledge fed which output. That traceability is what turns "the AI wrote this" into "here is exactly what it read and what it produced."

Keeping company knowledge secure and compliant

Feeding internal documents to AI raises an obvious question: where does that data go? AgentWorks is built EU-native, in the Netherlands, and treats data protection as a default rather than an add-on.

Before any content reaches a model, personally identifiable information is masked at the gateway, so raw PII isn't passed to the LLM. The platform uses EU model endpoints where offered for data residency, and works under no-training, zero-retention contracts so your knowledge isn't used to train third-party models or retained beyond the request. State-changing actions can require human-in-the-loop approval, and every action is written to an immutable, append-only audit trail you can export as CSV or JSON.

AgentWorks is EU AI Act-ready, with per-agent risk classification built in. That is deliberately "ready," not a blanket "compliant" claim: your actual obligations depend on how you use the system. You can dig into the details on the compliance and EU AI Act pages, or see the broader posture on the trust page. A DPA is available on request.

Summary: A company knowledge base grounds your AI agents in your own data using retrieval-augmented generation. On AgentWorks you upload PDF, DOCX, TXT, and CSV files or connect URLs, Notion, and Confluence; content is indexed with pgvector and answered with citations. Agents say "I don't know" when the answer isn't in your data, PII is masked before any model sees it, and everything runs EU-native with an exportable audit trail.

Frequently asked questions

What file types and sources can I add to an AgentWorks knowledge base?

You can upload PDF, DOCX, TXT, and CSV files directly, and connect live sources including public and internal URLs, Notion, and Confluence. Additional connectors such as Google Drive, OneDrive, and SharePoint are available through the platform's integrations, so knowledge can stay where your team already maintains it.

How does AgentWorks stop AI agents from hallucinating answers?

Agents answer using retrieval-augmented generation, pulling relevant passages from your knowledge base and citing them. When the answer isn't present in your data, the agent explicitly says "I don't know" instead of inventing one. Citations let a reviewer trace every claim back to its source document.

Is my company data used to train the AI models?

No. AgentWorks operates under no-training, zero-retention model contracts, so your knowledge isn't used to train third-party models or kept beyond the request. PII is masked at the gateway before any model sees it, EU model endpoints are used for data residency where offered, and a DPA is available on request. ===END======SLUG=== buy-vs-build-ai-agents ===META=== title: Buy vs Build AI Agents: The Real In-House Cost excerpt: A practical guide to the build vs buy AI agents decision, and the hidden cost of DIY governance, routing, RAG and audit versus a ready platform. seoTitle: Build vs Buy AI Agents: The Hidden DIY Cost | AgentWorks seoDescription: Should you build AI agents in-house or buy a platform? Compare the real cost of DIY routing, RAG, governance and audit against AgentWorks. category: Best Practices readTime: 8 min read pexelsQuery: engineering team whiteboard