Full disclosure: Advancer is an investor in Aiqbee. I am writing this because I genuinely believe the problem Aiqbee solves is one of the most underestimated challenges in enterprise AI adoption, and it is one I have experienced firsthand.

The Copy-Paste Problem

If you are using AI tools seriously, whether ChatGPT, Claude, Gemini, or any of the growing list of capable LLMs, you have probably hit the same wall I have. You start a conversation, paste in some context about your project or organisation, get a useful answer, and then close the tab. The next day, you open a different tool, paste roughly the same context (but not quite the same), and get a slightly different answer.

Multiply this across a team of ten people, each with their own preferred AI tool, each pasting their own version of the company's strategy document or product roadmap, and you have a real problem. The information is inconsistent. It drifts. Nobody is sure which version is current. And every single conversation burns through your context window with the same background material.

Context Limits Are a Hard Ceiling

Large language models have finite context windows. Even the most generous models top out at a few hundred thousand tokens, and practical performance often degrades well before that limit. When you are loading the same organisational context into every conversation, you are consuming a significant portion of that window before you even ask your first question.

For complex projects, this becomes a genuine constraint. You either leave out important context and get generic answers, or you include everything and leave little room for the actual work. Neither outcome is good.

The Real Value: Private Knowledge

Here is something that gets overlooked in the excitement about AI: the most valuable information for your business is almost certainly not on the internet. Your product roadmap, your internal architecture decisions, your customer feedback patterns, your competitive positioning, the lessons your team learned from that failed migration last year; none of this is in the LLM's training data.

Without access to this private knowledge, AI tools can only give you generic answers. They can tell you what the internet thinks about microservices architecture, but they cannot tell you why your team decided to use event sourcing for your particular domain, or what the trade-offs were when you chose your current cloud provider.

Aiqbee: One Brain, Every Tool

This is the problem Aiqbee solves. Aiqbee lets you create curated knowledge repositories called Brains, structured collections of your organisation's proprietary information organised as a knowledge graph. These Brains connect to any AI tool that supports Model Context Protocol (MCP), which increasingly includes the major players.

Instead of every team member maintaining their own collection of context documents, you maintain one Brain. When anyone on the team opens Claude, ChatGPT, or any MCP-compatible tool, they get the same consistent, current organisational context. No copy-pasting. No version drift. No wasted tokens on redundant background information.

GraphRAG: Smarter Than a Document Dump

Aiqbee does not just store documents and retrieve them wholesale. It uses GraphRAG (Graph-based Retrieval Augmented Generation), which means your knowledge is structured as interconnected nodes with relationships. When an AI tool queries your Brain, it gets precisely the relevant context, not an entire document that happens to contain one relevant paragraph.

This matters because it means you use fewer tokens, get more relevant answers, and can maintain much larger knowledge bases than brute-force document retrieval would allow.

Self-Hosted Control with Hive Server

For organisations with strict data governance requirements, Aiqbee offers Hive Server: a self-hosted deployment that keeps all your proprietary knowledge within your own infrastructure. Your data never leaves your environment, while still providing the same MCP integration that lets any AI tool access it.

This is particularly relevant for government agencies, financial institutions, and any organisation handling sensitive information. You get the benefits of AI-ready organisational knowledge without the compliance headaches of sending proprietary data to third-party services.

The Bottom Line

The organisations that will get the most value from AI are not the ones with the biggest budgets or the fanciest models. They are the ones that solve the context problem: making their private, proprietary knowledge consistently available to whatever AI tools their teams use, without redundancy, drift, or token waste.

That is why Advancer invested in Aiqbee, and it is why I think every organisation seriously adopting AI should be thinking about their knowledge infrastructure, not just their model selection.