Nirah
Feels Like the AI You Already Use. Knows What It Never Could.
Nirah looks and feels like the AI assistant your people already use. Type a question, get an answer. The difference is what it can reach: your company's own information, workflows, conversations, and expertise. Every answer carries its sources, and nobody sees more than their access allows.
The Problem
Your team already uses an AI assistant. It writes well and knows a great deal about the world. Ask it about last quarter's results, the client who pushed back in March, or how your company prices its work, and it has nothing. So people paste in fragments, fill the gaps from memory, and act on an answer built from half the picture.
How It Works
From input to insight in four steps.
Ask
Type the question the way you would ask a colleague. Nothing new to learn.
Reach
Nirah draws on the UniversalContext Map: your documents, meetings, conversations, and the systems connected to it.
Answer
The answer comes back with its sources. Open the document or the meeting it came from.
Continue
Follow up and refine. Nirah keeps the thread, and the company context behind it.
Key Capabilities
What you can do with Nirah.
The chat your people already know. Nothing to learn before the first useful answer.
Answers from your own contracts, projects, clients, and decisions, not from general knowledge alone.
Every answer links to the documents, messages, and meetings it came from.
Who has done this before, and how. Answers point to the people and the past work behind them.
Nirah sees what each person is allowed to see. Role-based access and zero data retention for AI traffic apply to every question.
Each question goes through whichever model fits the job, so your assistant is never tied to one vendor.
Use Cases
See how organizations apply Nirah in practice.
A new manager asks how the company handled the last three opportunities like this one and gets the proposals, the pricing notes, and the people who led them.
One question before the call: the open items, the recent conversations, and every commitment made to the client.
A clinician asks which version of a protocol is current and gets the document, the revision, and the decision that changed it.
See Nirah on your own kind of data
One conversation, a demo environment built on mock data modeled on your organization, and two weeks inside it to decide.
No pitch. No pressure. Just a 30-minute look at how this works.