Meshum
A unified knowledge layer where every team keeps using the tools they already have—but everything becomes searchable and actionable through AI agents.
Meshum: Fixing the Knowledge Nightmare Every Tech Company Has
If you work at a software company with more than a handful of people, you’ve experienced this: someone asks you a question, and you know the answer exists somewhere. It’s probably in a Confluence page someone wrote six months ago. Or buried in GitHub. Or in a Slack thread that got archived. Or just vibing in someone’s head because it never got written down.
So you spend an hour digging through tools. Or worse, you ask in a meeting and interrupt someone who definitely knows but was trying to work.
That’s the problem Meshum is built to solve.
The Real Cost of Knowledge Being Everywhere
The pitch is easy: “Stop searching.” But the actual cost is way bigger than just wasting time on searches.
Developers spend hours in meetings answering the same questions. Not because they’re bad at saying no, but because nobody knows who else might know, or that the answer is documented somewhere. Architects wait for input from other teams and it takes days. New hires spend weeks context-switching between tools trying to understand how anything works. Critical context about why a system was built a certain way gets lost when the person who built it leaves.
And when you’re in an incident? You’re frantically searching for runbooks while everything’s on fire. You know someone fixed this before, you just don’t know where.
What Meshum Actually Does
The idea is straightforward: connect everything your organization already uses—Confluence, GitHub, Jira, Slack, Notion, whatever—into a unified knowledge graph. Then throw AI agents at it.
But here’s the key: you’re not ripping out your existing tools. Developers keep using GitHub the way they always have. Architects keep writing in Confluence. Teams keep their Jira boards. Everything just becomes connected and queryable at the knowledge layer.
The unified graph understands relationships. That ticket is related to this code. That code implements this architecture decision documented in Confluence. That Slack thread was about this exact problem. The system sees the connections your tools never will.
Then you get AI personas scoped to specific teams. The infrastructure team’s persona understands architecture, deployment systems, and how your infrastructure actually works. The backend team’s persona knows the codebase, patterns, decisions. They answer questions about their domain without hallucinating because they’re grounded in actual context.
And it gets real when you need cross-team answers. A query that spans architecture decisions, code implementation, deployment strategy, all of it orchestrated across the right personas without pulling people into a meeting.
Why This Matters
The boring part of software work is answering questions that have been answered before. The interesting part is building things.
Right now, knowledge bottlenecks block work. You can’t make a decision until you talk to the right person. That person isn’t available, so the decision waits. A new hire asks a question that takes thirty seconds to answer from docs, but docs don’t exist, so it takes thirty minutes in a meeting instead.
Meshum removes those bottlenecks. Not by forcing people to write better documentation or use different tools. By connecting what already exists and making it queryable.
For new hires specifically, the upside is wild. Instead of “read this 50-page Confluence wiki,” they get dynamic guides generated from your actual documentation and codebase. Relevant to what they’re currently trying to do.
For incident response, you’re not searching while panicking. The system surfaces relevant runbooks, past incidents, and code context instantly. Everyone’s focused on actually fixing the problem.
The Technical Reality
This isn’t a simple product. You’re parsing Confluence APIs, GitHub webhooks, Jira integrations, Slack history. Every tool has different auth, different formats, different update patterns. The unified graph has to understand semantic relationships across completely different data sources.
Then you’re building AI agents that are actually useful. Not just “summarize everything,” but genuinely scoped personas that understand their team’s context deeply enough to give answers instead of hallucinations.
The webhook-first architecture means knowledge graph updates propagate in real-time. You update a Confluence page and fifteen minutes later the AI persona has the new information. You merge code and it shows up immediately.
And because this is sensitive—your entire knowledge base in one place—it’s built from the ground up with the assumption that your data stays yours. Use Azure OpenAI, Anthropic, whatever LLM you trust. Your knowledge doesn’t get sent to some random SaaS company.
Why Now?
The infrastructure for this didn’t exist five years ago. LLMs couldn’t ground themselves in external knowledge reliably. API integrations required more effort than value. Webhook infrastructure was worse.
Now? LLM context is deep enough to understand large documentation. API-first tools are the norm. Multi-agent orchestration is actually feasible.
More importantly, the problem got worse. As organizations got bigger and more tools got added, knowledge fragmentation got worse. The cost of questions going unanswered went up.
Meshum exists because the time was right and the problem had become urgent enough that building the solution made sense.
The Current State
There’s a website. There’s a clear pitch. There’s real interest from teams who are living this problem every day. The GitHub org exists, but this is early—the real work is just starting.
That’s the honest truth. Some teams are on the waitlist. There are conversations with organizations that want this badly enough to be willing to work through early versions.
Building something that connects to ten different tools and does it reliably is non-trivial. Building AI agents that are actually useful instead of confidently wrong requires tuning. Getting webhook syncs solid so knowledge actually stays fresh takes iteration.
But the foundation is there, and the problem is real enough that people are willing to work on it.
Who This Is For
Tech companies that have outgrown the “everyone talks to everyone” phase but haven’t built infrastructure to deal with that growth. Teams frustrated with tribal knowledge. Organizations that have invested heavily in documentation but can’t find it when they need it.
Basically, if you’ve ever been in a meeting that shouldn’t have happened because the answer was documented somewhere, or spent an hour searching tools for information you know exists: this is for your company.
Check it out: meshum.elixus.be
Early stage, but if you’re interested in joining the waitlist or want to talk about what you’re struggling with: they’re listening.
The GitHub org: github.com/Meshum