MightyRAG
What it is
MightyRAG is a governed AI knowledge workspace. It takes the knowledge scattered across your organization, documents, databases, and the institutional memory that lives in people's heads, and turns it into two things: answers your team can verify, and automated actions your team can trust.
Most "chat with your documents" tools stop at plausible-sounding answers. MightyRAG is built for organizations where plausible is not good enough. Every answer carries its citations, organizational knowledge passes through human review before it becomes the official record, and any action an agent wants to take can be gated behind an approval step.
It is local-first and multi-user by design. You can run the whole platform on your own hardware with local models, or connect it to the provider of your choice. Either way, the governance stays the same.
Who it is for
Operations directors who want reliable answers from messy internal knowledge. Knowledge and compliance managers who need a review trail. Analysts who ask hard questions all day. CIOs and CISOs who will not sign off on a black box.
What it does
- Two-stage retrieval with re-ranking. A calibrated cross-encoder re-ranks candidate passages, so the best evidence surfaces, not just the closest match.
- Cited answers. Responses point back to their sources, so verification takes seconds.
- Governed Organization Knowledge. Proposed knowledge goes through human review queues before it becomes shared truth.
- Durable memory and a knowledge graph. A relational knowledge graph canvas lets teams see and build how facts connect.
- Agentic workflows with approval gates. Agents can act through MCP and OAuth-connected tools, with human sign-off wherever you require it.
- Multi-tenant workspaces with RBAC. Teams, roles, and permissions keep the right knowledge with the right people.
- Model-agnostic. Run local models, or connect OpenAI, Anthropic, Gemini, or Azure. Switch without rebuilding.
Under the hood
A Deno backend and Angular frontend, shipped as a Docker Compose stack, with a full Diátaxis documentation set, a software bill of materials, and standalone architecture diagrams. Retrieval, memory, and organization knowledge are documented down to the API endpoint and data model. The agent runtime builds on the Strands SDK.
Interested?
If your team spends its days hunting for answers that already exist somewhere in the company, we should talk.