If you're a developer, engineering leader, or product manager thinking about how AI should interact with organizational knowledge, we'd love to chat and learn from your perspective.
#EnterpriseAI#AIAgents#EnterpriseSearch#KnowledgeGraph#OpenSource #RAG#contextgraph
Most companies own their data. Very few own their context.
Documents, emails, chats, tickets, databases, and applications contain information, but the real value lies in the relationships between them.
That's where the Enterprise Context Graph comes in.
A context graph connects people, teams, projects, documents, conversations, customers, and systems into a unified layer of organizational knowledge.
Without it:
• AI agents operate in silos
• Search lacks business context
• Knowledge gets fragmented across tools
• Every AI application rebuilds context from scratch
The Enterprise Context Graph is quickly becoming a foundational layer of the modern enterprise stack.
The future won't be a collection of disconnected AI tools.
It will be a shared context layer that every application and agent can build upon.
Own your data. Own your context.
At PipesHub, we're building an open-source and extensible Enterprise Context Graph platform that enables organizations to own, control, and extend their context layer while powering enterprise search, AI agents, and knowledge applications on top of it.
The future won't be a collection of disconnected AI tools.
And it shouldn't be locked inside a proprietary platform.
Just as companies own their source code and data, they should own the context layer that powers their AI systems.
Under the hood, PipesHub works like the context engine for enterprise AI. The knowledge graph connects company knowledge, the ranking layer finds what matters, permissions keep access safe, and SDKs, MCP support, and Agent Builder let developers and teams build agents on top.
The result:
- Less guesswork. More verified intelligence.
- Accurate answers even with smaller models.
- Agents that understand your business context.
This is not just enterprise search. This is the foundation for how the next generation of people at work will use AI at scale.
PipesHub 1.0 is our baseline. Every release from here builds on it.
Enterprise AI does not need another chatbot.
It needs a context layer.
Today, we are introducing PipesHub 1.0: The Context Layer for Enterprise AI.
We redesigned how enterprise search feels.
Not just the UI.
The way teams ask, verify, and act on company knowledge.
PipesHub is built on three foundations:
Trust it
Answers should stand on evidence. Every response is grounded, cited, and traceable down to the source.
Own it
Your data. Your models. Your infrastructure. PipesHub is open source, self-hostable, and built for enterprises that want control.
Build on it
Search is context. Agents are workers. Teams can build AI agents that reason, cite, and act across Slack, Jira, Google Drive, Microsoft 365, Salesforce, databases, and more.
We’re excited to share that Pipeshub is SOC 2 compliant (Type I) & we’re under observation for SOC 2 Type II until early February.
OSS: https://t.co/kdTcMwYDop
Get started today: git clone && docker compose
That's it.
OSS : https://t.co/kdTcMwYDop
Join our discord : https://t.co/thvVDbVRzk
Book a Demo : https://t.co/Xp4zMUWrCt
When you ask your assistant or your junior's to do something, do you give them incomplete information and then expect them to answer ?
Probably not.
So why do we do that to LLMs ?
The Agent plans and navigates the path to find the right context instead of answering from incomplete data.
The result is higher accuracy and correctness, with 100% citations across all file types and business apps.
Instead of blindly throwing chunks to an LLM, we let Agents see the query first and then decide which tools to use Vector DB, Full Document, Knowledge Graphs, Text to SQL, and more and formulate answer based on the nature of the query.
Most RAG frameworks simply dump chunks of data to an LLM and expect it to answer correctly from incomplete context.
At @PipesHub, we have completely rethought how RAG should work.