To answer questions about your company, projects, or domain, AI Agents need access to the relevant information. Attaching a PDF to a ChatGPT conversation works up to a point. Using plugins or connectors with Claude, Cowork, or Codex works a bit better. It is still not enough.
AI models are trained on public knowledge. When they do not know something, they use tools and search engines to find it. The answer is only ever going to be as good as the information they can gather. If they cannot find what they need, their answers and actions will not be good enough.
Companies actually have a lot of data and knowledge. It is just squirreled away in archives, awkward file formats, books, company drives, old tickets, project folders, and tools that were never designed for agents. Even people struggle to navigate that maze. An AI with generic API access to email, Google Drive, and a few SaaS tools will struggle too.
Give Agentic AIs the company facts and they can do impressive things. Starve them of context and even a model solving Nobel Prize-level problems becomes useless inside your business. The model is not the bottleneck. The knowledge is.
Knowledge Bases Provide Operating Context
A common solution in AI workflows is a knowledge base: a data store with a search engine in front of it. Modern information retrieval systems can do semantic search. They can look beyond keywords and take meaning into account. This can work for text, images, documents, tickets, and other company material.
We are all used to good search when we use Google. But Google does not cover your company data. Many SaaS tools have search APIs, but they usually do not understand how your company organizes information, which version is final, which document is a draft, which customer record matters, or how different pieces of information relate to each other.
Getting Accurate Facts Without Blowing Through Your Token Budget
When presented with a complex question and , AI Agents can query the data store, gather relevant information, and attach it to their context. This is Retrieval Augmented Generation, or RAG for short. A lot of AI workflow products are built around RAG and vector search for a good reason: this is how they unlock company knowledge.
Conceptually, RAG is search. Any decent search engine can be used for it. The hard questions are practical: can it find the relevant information, rank it properly, and dig out the best version of what the agent needs?
Modern AI models have huge context windows, so they can make sense of a lot of material. But context costs tokens. If search is lousy or slow, agents compensate by gathering too much and trying to reason through the pile. The more they gather, the more expensive and noisy the workflow gets.
Knowledge Bases Are The Leverage Point
A well organized knowledge base changes the economics of agentic work. If agents struggle to find what they need, they are slow and ineffective. Give them clean, relevant, well-ranked knowledge and many niche company problems become tractable.
That is the work FORMATION XYZ does. Jilles van Gurp, the CTO behind FORMATION XYZ, has worked with information retrieval systems for over two decades. Across his career, he has used search frameworks like Apache Lucene and products such as Elasticsearch and Solr to build domain-specific search engines for clients and startups. Lately, that includes vector search. He also created the search library that powers search on this website.
Our mother company FORMATION relies heavily on search as well. The Spatial Intelligence platform turns location and spatial context into operational knowledge for workers in factories. That work was the spark for FORMATION XYZ. If we can do this for factory workers, why not do it for everyone else?
Creating And Maintaining The Knowledge Base
The FORMATION approach starts from value, not from a tool demo. What questions does the knowledge base need to answer? What does the team need to do? Which information exists already? Which parts are authoritative, stale, duplicated, private, or simply noise?
There are no shortcuts here. No magical download lets you skip understanding the company. We have to map the processes, the people, the documents, the decisions, and the knowledge that makes the business work.
Then we build prototypes and run data experiments. We look for the right structure, the right search approach, and the right level of curation. Sometimes that means extracting fields from messy documents. Sometimes it means tagging product lines, normalizing customer names, filtering old versions, or enriching records so agents can use them safely.
This often leads to automated data pipelines. In plain English: take raw company information, clean it up, reshape it, and load it into the knowledge base in a form that works for RAG. This is not a one-time job. The company needs to own the knowledge base over time. Your workers and AI Agents can only be as good as the knowledge they have at their disposal.
Building Agentic Workflows
Once the knowledge layer works, we map processes and design Agent Workflows around them. Agents are tool-using AIs. They can manipulate documents, call deep into APIs, update systems, and affect the real world.
That has to be constrained with guard rails. Guard rails keep the right people in the loop, especially at decision points where an agent should not act alone. They also prevent agents from doing the wrong thing quickly and confidently, which is the nightmare version of automation.
How FORMATION XYZ Helps
A lot of companies are trying to make sense of the hype and get value out of the AI tools available to them. Business leaders are anxious about missing out, falling behind, or watching competitors move faster. AI FOMO is very real.
We can help. We are senior product designers, information retrieval experts, and we know how gnarly the real world can be for companies. Our way is not pushing you onto a tool. We work with your team to figure out what is needed, what to build, what to borrow, what to keep, and what expectations are realistic.
If your company is ready to turn scattered internal knowledge into practical AI workflows, contact us . We can help identify the highest-value use cases, design the knowledge base, and build the retrieval and ETL processes needed to support agents that work with your business context.