Straight answers about AI memory, shared context, and real work.
Nine questions people ask over and over, each answered directly. Every page starts with the problem, gives real credit to what people already do about it, explains the point where that stops working, and ends with the AI doing something rather than saying something. Useful whether or not you ever use HexaHQ.
Keeping what the AI has been told
Why context disappears between sessions, people, and AI products, and what to do about each.
How do I give ChatGPT or Claude persistent memory across sessions?
The model cannot remember. What that leaves you: a durable store outside it, and a rule for what comes back on the next task.
How do I share AI context across a team?
Getting it out of private chats is the easy half. Keeping one current version, and giving each person only their part, is the rest.
How do I stop losing context when I switch between ChatGPT and Claude?
Neither vendor will make its memory portable to the other. Keep the durable rules in a layer of your own instead.
Do I need to build my own AI memory system?
For one person, a DIY stack is hard to beat. Five things turn it into a product, and they all arrive at once.
Making the AI get it right, then letting it act
Corrections that last, retrieval that contradicts itself, who should receive which rules, and how to let AI finish the work.
Why does AI keep making the same mistake?
The correction went into the conversation, not into anything the next conversation reads. What it takes to make one stick.
Why does enterprise RAG return outdated or conflicting answers?
Relevance has no opinion about which document is current. The five questions an index cannot answer.
How do I give AI role-based access to company knowledge?
One shared context layer, with different rules reaching different people, so the useful material can be in it at all.
How do I safely let AI update our business systems?
The rules before the decision, and a policy on the write. Read-only agents and broad service accounts each miss half.
Why do AI agents go off track on long, multi-step work?
One wrong assumption at step one is carried into every step after it. More supervision is the expensive fix.
If you already know what you are comparing
These pages start from the problem. When you are weighing a particular product or approach instead, the comparison pages start there: Obsidian, Google Drive, GitHub, Glean, native ChatGPT and Claude memory, and the AI-employee idea, each answered on its own terms.
One shared context for people and AI to get work done together.
HexaHQ automatically captures how your organization works, learns from corrections, and gives AI the context it needs to make decisions and take action the way your team would.