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Connect your knowledge
Review every AI update

Let AI Agents truly use your enterprise knowledge while keeping final change authority in human hands.

OMI connects Notion, GitHub, websites, and other knowledge sources to give Codex, Cursor, and AI Agents unified context. Every knowledge change is first organized as a reviewable proposal before becoming organizational knowledge.

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  • No migration from your current knowledge stack
  • Works with your existing agent workflows
  • Every update is reviewed before durable write
Notion
GitHub
Website
Docs

OMI

read path

Unified knowledge package with source references, evidence, and freshness.

Codex / Cursor / OpenClaw / …

Agents read trusted context for real tasks and produce outputs with evidence links.

Proposal Review

write path

All knowledge updates become proposals and require review before durable write.

Humans / Review Agents

People or dedicated review agents decide whether proposals become durable knowledge.

Approved Knowledge

Human-reviewed decisions become trusted organizational knowledge.

rejected / pending

Knowledge update proposal

Pending review

Changed Section

+ Add troubleshooting step for context retrieval mismatch

- Remove outdated workaround for legacy API

source: github/issues#108source: docs/v3-support.md

You may have already started using AI agents for real work, but you’re likely not ready to let them safely use and update knowledge.

Knowledge is fragmented.

Docs, code, and tickets live in different systems. Agents reason from partial facts—and you discover the gap only after the wrong answer is in front of a customer.

Agents keep going with partial context.

Models do not pause when context is thin; they still sound certain. Every missed edge case becomes rework, escalations, and trust debt.

New experience appears every day.

Support wins and incident learnings pile up in chats and notes. Without a durable write path, the organization keeps re-learning the same lesson.

Most teams still govern this manually.

Copy-paste, Slack threads, and last-minute human review are the real “workflow.” It breaks the moment headcount or incident volume ticks up.

The bottleneck is rarely model capability now. It is usually weak knowledge access, unstable output trust, and missing governance for updates.

OMI provides not just another knowledge base, but a governance mechanism for AI Agent knowledge operations.

Connect existing knowledge

Read path

Start from Notion, GitHub, and websites without forcing migration into a new system.

Unified context for agents

Source-backed context

Serve Codex, Cursor, OpenClaw, and other agents with context packages that include sources and freshness.

Proposal review for updates

Review boundary

Convert AI-generated knowledge changes into proposals and support review / approve / reject decisions.

Trusted knowledge loop

Durable knowledge

Turn one-off outputs into a governed and continuously improving organizational knowledge loop.

Product Loop

From knowledge access to knowledge accumulation, build a governed operating loop.

  1. Step 1

    Connect

  2. Step 2

    Retrieve

  3. Step 3

    Analyze

  4. Step 4

    Propose

  5. Step 5

    Review

Connect existing knowledge sources

Provide unified context for agents

Read context in real tasks

Organize new AI-derived insights into proposals

Review before writing durable organizational knowledge

AI can participate. Humans approve.

OMI does not ask your team to replace existing systems. It adds the missing layer required in the AI era.

You do not need to

  • Build a new knowledge platform
  • Replace your documentation stack
  • Force teams to abandon existing agent tools

You need to

  • Connect existing knowledge sources
  • Provide usable context to agents
  • Review every knowledge update before write

Use Cases

Support, creative agents, and ops—same governed knowledge layer.

Whether you ship answers, assets, or on-call context, agents need unified retrieval and a safe path from AI insight to durable knowledge.

Creative & growth agents

Teams use AI for campaign copy, social posts, and standout product imagery. When brand facts, product truth, and tone live in one place—and every improvement can be reviewed—output quality jumps by orders of magnitude.

  1. Brief
  2. Shared KB
  3. Agent
  4. Copy & visuals
  5. Reviewed updates

Who it serves

  • Marketing and growth operators
  • Owners of product and brand knowledge

Typical gaps today

Connection & duplication

Background knowledge sits in Feishu, enterprise chat drives, laptops, and ad-hoc files. People paste the same context into prompts again and again. Even with MCP tools, sources are so fragmented that agents do not know where to look first.

AI write-back without review

Iterations turn into lessons, guardrails, and "permanent" brand memory. That is an enterprise governance problem: someone must review before those writes become truth. Most stacks cannot express that boundary.

Pre-built expert knowledge

Specialist firms or other teams may already have playbooks that should guide your agents. A platform can deliver that as vetted, consumable knowledge—almost like a shared pre-trained layer—so you do not rebuild generic industry depth from scratch. Providers can package and sell that expertise safely.

If your team already uses AI Agents in real workflows, a knowledge governance layer is not optional.

OMI puts enterprise knowledge into agent workflows and keeps every update inside a reviewable, traceable governance boundary.

Provide at least one: email or phone.

Your role (select all that apply)
How do you plan to use OMI? (select all that apply)

Start from a single high-value workflow, then expand into an enterprise-wide AI knowledge governance layer.