🚀 Introducing JoinClaw — your own private OpenClaw, powered by your private data. Run Agentic AI without exposing your personal machine or sacrificing control. Built for builders, teams, and organizations that want more control, privacy, and ownership over their AI systems.

RAG helped AI find company documents. The next step is harder: Can the agent tell what’s current? Who is allowed to see it? Where did it come from? Can we audit what happened? That’s the layer Joinable is building.

A chatbot can answer a question. An enterprise agent may actually take action. That changes the requirement. Before agents become more powerful, the knowledge underneath them needs to become more trustworthy.

Enterprise AI needs more than retrieval. It needs: → Permissions → Provenance → Validation → Freshness → Auditability That’s what makes knowledge agent-ready.

Most companies have an AI problem disguised as a data problem: Their most important knowledge was written for humans, not agents. Propagator turns that knowledge into something AI can safely reason from.

AI agents shouldn’t have to guess how your company operates. They should know. Your policies. Your procedures. Your permissions. Your institutional knowledge. That’s the idea behind Joinable.

The next enterprise AI breakthrough isn’t another model. It’s making the knowledge underneath the model trustworthy. That’s what we’re building at Joinable.

Your company already has the knowledge your AI needs. The problem? It’s buried in PDFs, emails, tickets, Slack threads and spreadsheets. Joinable turns that scattered information into governed knowledge AI agents can actually use.

Hot take: By 2027, “we have evals” will sound like “we have tests.” Table stakes. The teams building eval infrastructure now will own the next decade.

Be honest — how does your team evaluate AI today?

Finish the sentence: “The most underrated part of building AI is ______” 👇

85% of business data is unstructured. 85% of AI projects fail before production. These two facts are related.

Joinable by the numbers: → 1,400+ AI Builders → 400+ AI systems → 1,000,000+ verified contributions The last mile, crossed at scale.

Fine-tuning a private LLM on your documents? The model learns your data. Humans verify it learned the right things. Skip the second step and you ship the first step’s mistakes.

Software can A/B test its way to quality. Robots can’t A/B test their way to safety. Embodied AI needs human evaluation before deployment — not after incidents.

Internal AI assistants fail differently: Wrong answer to a customer = bad review. Wrong answer to your CFO = bad decision. HR, legal, finance, support — evaluate accordingly.

Your RAG app is only as good as its worst retrieval. Automated metrics miss what humans catch: wrong context, confident nonsense, subtle staleness. Human evals close the gap.

Proof-of-human-work: Verifiable evidence that real, qualified people evaluated your AI. The receipts — on-chain.

“Where did this training data come from?” If your vendor can’t answer that, you have a problem. If it’s on-chain, you have an answer.

Trust in AI data shouldn’t require trust in a vendor. Every Joinable contribution is recorded on-chain: who · what · when · verified Audit trails > promises.

AI bug bounties: Point our tester community at your AI app. They break it. You fix it. Before your users find out.