A generic model answering from its training data will eventually get a policy or product detail wrong. Lira grounds every answer in the documents and data you upload, so agents answer from what's actually true for your business.
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You upload documents — product specs, policies, FAQs, pricing sheets — or connect structured data sources, and the agent retrieves the relevant pieces to ground its answer whenever a customer asks something the knowledge base covers. When something isn't in the knowledge base, the agent says so rather than guessing, and can escalate to a human instead.
Upload PDFs, docs and spreadsheets directly — no manual reformatting required.
Connect databases and structured data alongside unstructured documents.
The agent retrieves the relevant passage before answering, rather than relying on general knowledge.
Update a document and the agent's answers reflect it immediately, no retraining required.
Your documents stay within your account's knowledge base, not shared across customers.
When a question falls outside the knowledge base, the agent says so instead of guessing.
Answer customer questions accurately from your actual product documentation.
Build employee-facing agents grounded in internal wikis and policy documents.
Keep agent answers current by updating source documents, not retraining a model.
PDFs, Word documents, spreadsheets and plain text are all supported for upload.
As current as the last document update — there's no retraining delay between updating a source and the agent using it.
Every new account starts with 100 free tokens — enough to build and test-call your first agent before spending a rupee.
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