Foundations
Knowledge systems that answer from your own documents
Every company has answers written down somewhere: the HR policy, the pricing rules, the SOP for a return, the contract clause, the product spec. Finding them is the problem. We build retrieval systems that answer in plain language and point at the page they got it from, for your staff, your agents and, where you choose, your customers.
- Hybrid search over your documents
- Permission-aware answers
- A citation on every answer
In short
A knowledge system (retrieval-augmented generation) indexes your documents and records, finds the passages relevant to a question with both keyword and semantic search, and has a language model write the answer from those passages only, with citations. It respects who may see what, so a staff member gets the policy and a manager gets the policy and the exceptions. Nothing is answered from memory, and it is the same layer our voice, task and customer agents answer from.
What we build
A staff knowledge base over HR policies, SOPs and handbooks, answering questions such as “how many casual leaves can I carry forward” with the clause quoted. A product and pricing assistant over your catalogue, specifications and price lists, for your sales team or your customers. A contract and document search for the finance and legal side.
Each one is built on your documents as they are: PDFs, Word files, spreadsheets, tickets, emails and database records. We clean and chunk them properly, because answer quality is decided there, not in the model.
- HR policy and SOP assistants for staff
- Catalogue, specification and pricing assistants
- Contract and document search
- Ticket and email history search
- Questions and answers in more than one language
How we build it
Retrieval comes first. We combine keyword and vector search, rerank the results, and give the model only what it needs; every answer carries its sources, and an answer with no source is refused rather than invented.
Permissions are enforced at retrieval time: the system never fetches a passage the asking user could not open. We measure answer accuracy against a set of real questions with known answers before launch, and track it afterwards as documents change.
- Chunking and indexing designed per document type
- Hybrid retrieval with reranking
- Citation-enforced generation
- Permission checks at retrieval
- Accuracy measured on real questions
What it connects to
Shared drives and document stores, your ERP, CRM and HR systems for live records, ticketing and email, and a chat surface your team already uses: WhatsApp, the browser, or inside AurumOne and the CRM.
- Shared drives and document stores
- ERP, CRM and HR records
- Ticketing and email
- WhatsApp, web and in-app chat
Where it shows up
Automation playbooks this service runs in
Knowledge & Document AI
Common questions
Is this the same as a chatbot?
No. A chatbot answers from the model’s general knowledge. A knowledge system answers only from your documents, shows where the answer came from, and says so when the answer is not there. That difference is what makes it usable for policy, pricing and contracts.
Our documents are a mess. Does that matter?
It matters most. Much of the work is cleaning, de-duplicating and structuring what you have, and deciding which version of a document is the truth. We do that with you first; a model cannot fix a contradictory source.
Can it work in Hindi and Gujarati?
Yes. Questions can be asked and answered in English, Hindi and Gujarati, including against documents written in English.
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