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Solution Package
Custom-designed AI assistants trained on your internal documentation to automate employee inquiries and reports safely.
Empower your workforce with a private, secure AI assistant. Unlike public models, NANO4C's Enterprise AI Assistant links directly to your internal file servers, SharePoint, and databases—giving employees instant, accurate answers while keeping data safe within your network.
How our Enterprise AI Assistant integrates into your environment.
Internal files are converted to vectors and stored in a private database (e.g. Milvus or Pinecone) inside your VPC.
A secure gateway that routes user queries to private LLM models, applying strict filtering to block sensitive inputs.
The assistant queries the vector database first to find correct context, feeding it to the model to generate accurate answers.
Employees get accurate answers, summaries, and document translations in seconds instead of hours.
Private hosting guarantees your files are never used to train public models or shared outside your cloud.
New hires ask the assistant for policies, HR rules, and code structures, speeding up onboarding.
All data, vectors, and models are hosted inside your secure private cloud. Data is never shared or trained publicly.
Yes, the system reads PDFs, Word files, Excel spreadsheets, TXT, HTML, and markdown documents.
Yes, the underlying models are optimized to understand and respond in both English and Arabic.
Yes, we integrate with SharePoint, Google Drive, local file shares, and SQL databases.
We use strict Retrieval-Augmented Generation (RAG) to ensure the assistant only answers based on your documents.
Yes, the system respects your existing Active Directory / LDAP groups, showing answers only from files the user has permission to see.
We deliver a working assistant connected to a test document library in 4 to 6 weeks.
We build web interfaces and integrate the assistant directly into Microsoft Teams, Slack, or internal portals.
Not necessarily. We optimize models to run on standard cloud instances or existing GPU servers.
Yes, we monitor query accuracy, retrain models, and manage system updates under our SLAs.