Enterprise LLM Assistant
Company Knowledge Should Be Askable, Not Buried in Folders
The procedures, technical manuals, contracts and past correspondence an organisation accumulates are an enormous asset — but only to those who know where they are. New joiners do not know whom to ask, and experienced staff answer the same question for the hundredth time.
We deploy assistants that work over your company documents using RAG (Retrieval-Augmented Generation). The assistant does not invent answers; it finds the relevant document, answers from it and shows the source, so accuracy stays auditable.
Key Capabilities
Corporate Knowledge Base
Procedures, technical manuals, contracts and training material are processed into a searchable knowledge base. PDF, Word, Excel and scanned documents supported.
Answers With Citations
Every answer states which section of which document it came from, so users can open and verify the source and fabricated information is largely prevented.
Permission-Aware Access
The assistant answers only from documents the user is entitled to see, so HR, finance or board material never leaks to unauthorised staff.
Multi-Channel Integration
Access through a web interface, intranet, WhatsApp or your existing helpdesk, so employees ask questions in the channel they already use.
On-Premise Deployment Option
Open-source models can run on your own servers. No data leaves the organisation, which suits institutions where regulation forbids data export.
Usage Analytics & Gap Detection
Most-asked topics and questions the assistant could not answer are reported, exposing the gaps in your documentation.
Where It Is Used
- HR policy and employment questions
- Technical service and troubleshooting guides
- Quality procedures and ISO documentation
- Product knowledge assistant for sales teams
- Regulation and contract enquiries
- New starter onboarding support
- Dealer and customer support lines
- Regulatory guidance in public institutions
How We Work
Content Inventory & Scope
We agree which document set feeds the assistant and which question types it is expected to handle.
Processing & Knowledge Base Build
Documents are chunked and indexed into a vector database, with scanned records converted to text by OCR.
Testing & Quality Tuning
Real questions are tested; incorrect or incomplete answers are analysed and the retrieval and answering strategy tuned.
Launch & Monitoring
The assistant opens on its channels and usage is monitored, with the knowledge base updating automatically as new documents arrive.
Preventing Fabrication Is an Architectural Matter
Language models producing confident wrong answers on topics they do not know is a well-documented risk. RAG architecture and mandatory citation tie the assistant to the documents — if the answer is not there, it says it could not find one.