Corporate AI with RAG: Internal Knowledge at Your Fingertips
Stop searching for documents for months. Just ask. The AI searches your entire internal knowledge base, and doesn't hallucinate because it cites real sources.
Every company above a certain size has the same problem: knowledge exists but is not accessible. Manuals no one reads, contracts lost in folders, best practices buried in Slack. RAG systems (Retrieval-Augmented Generation) solve this by linking AI directly to your documents.
The most common challenges
Employees waste hours searching for internal knowledge
Onboarding, compliance questions, technical docs: every search costs time. New hires are especially affected, they often don't even know where to start looking.
AI chatbots hallucinate and give false information
General models invent answers when they lack a basis. In business-critical questions, this is unacceptable. RAG ensures the AI only responds based on your real documents, with source citations.
Knowledge is lost when employees leave the company
If the only person who knows a process leaves, the knowledge is gone. A RAG system makes documented knowledge permanently accessible regardless of staff turnover.
The CCsolutions approach
CCsolutions builds RAG systems that index your entire document repository: PDFs, Word, Confluence, Notion, SharePoint, and internal wikis. The AI performs semantic searches, it understands questions, not just keywords.
The system runs on-premises or in your private cloud segment. Every answer includes a source citation and direct link to the original document. Employees can ask via browser, Slack, or Teams.
Access rights are preserved: those without access to a document won't get answers from it. The system integrates with your existing identity management (SSO).
Technologies
Frequently asked questions
What is the difference between RAG and a normal chatbot?
A normal chatbot answers based on its generic training, which can be outdated. RAG accesses your current documents live and cites exactly from the source.
How long does implementation take?
An initial RAG system with 5,000-10.000 documents can be deployed in 4-6 weeks. Efficiency grows as the repository expands.
Does the system stay updated as new documents are added?
Yes. The index updates automatically via automated crawling or webhooks from your document management systems.
Ready to get started?
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