Why Lorah
exists.
AI work should not reset every time you open a new tool. Lorah was built for people who already use AI seriously, but feel the hidden cost of working across disconnected tools.
One conversation in ChatGPT. Another in Claude. Research in Perplexity. Implementation in a coding assistant. Notes, documents, screenshots, meetings, and decisions somewhere else. The models are powerful. The work still fragments.
Lorah exists because serious work needs continuity.
Built by Yoram Golandsky
Lorah was founded by Yoram Golandsky, a cybersecurity executive and AI builder with more than 25 years of experience in security, risk, governance, and technology leadership.
That background shaped Lorah’s design. The product starts from a security operator’s instinct: know where data lives, know who can see it, know what became durable truth, and keep the human decision-maker in control.
Lorah is built by LaGuardAI, whose broader work is focused on AI trust, governance, security, and control. Lorah brings that same trust-first posture to the individual workspace.
What Lorah is
Lorah is a secure, local-first AI workspace where serious work keeps its memory. It lets you create persistent AI specialists, work across multiple model providers, review what should become durable project memory, and return to a project without rebuilding the same context every time.
Lorah is not a hosted AI model. It is not an autonomous agent platform. It is not a generic chatbot wrapper. It is a private desktop workspace for people who want their AI work to continue across sessions, tools, and decisions.
How we make money
Lorah is free to download and free to keep using. You do not need a credit card to install it. You do not need to provide an email just to try it. The free version is not a fake product. It is a real way to use Lorah with limits. Pro is for people who want all their teammates active at once, plus Sync, Remember this, Rooms, Background Tasks, and MCP.
Lorah charges for the workspace: the memory system, the review flow, the teammate structure, the local app. Lorah does not charge for model usage. You bring your own API keys for the model providers you choose, such as OpenAI, Anthropic, Gemini, Perplexity, or xAI, and you pay those providers directly at their published rates. No markup, no commissions, and no financial reason to push you toward one model over another.
If Lorah is useful enough for your serious work, Pro should be worth paying for. If it is not, the product has not earned it.
For early users
Lorah is currently in beta. That means the product is real, installable, and already useful, but still early enough that feedback matters.
The best way to try Lorah is simple: start with one serious project. Do not move your whole life into it on day one. Create or import one workspace, add a few specialists, ask a room or run a Sync, review the memory Lorah proposes, and see whether the next session starts with less re-explaining than the last one.
That is the test. Your work should compound. Your AI tools should stop acting like strangers. Your thinking should continue.