Architecture: RAG systems
AI knowledge base for business
Turn scattered documentation and corporate records into a single source of instant answers. We ensure complete confidentiality of operational data and zero AI hallucinations.
Knowledge Hub Pipeline
source synchronization
Secure and governed AI knowledge base
We build isolated RAG systems that operate exclusively on verified, internal data assets within your organization.
Isolated corporate data
The system processes only verified documentation, SQL databases, and spreadsheets. We guarantee absolute data confidentiality with zero risk of leaks to public LLMs.
Source-backed replies
Every generated answer includes a precise reference to the source document. This instantly verifies the truth and completely excludes AI hallucinations.
Access governance (RBAC)
The architecture strictly respects corporate access levels. The system answers only from sources the employee is formally authorized to use.
RAG securely connects language models with your data sources
The system completely eliminates the risk of generating false information. Instead, it searches corporate assets and transmits them as flawless context.
Resource mapping
Secure data ingestion from SQL databases, CRM/ERP platforms, and local file storage.
Vector indexing
Converting traditional documentation into high-performance semantic search repositories.
Governance and citation
Generating precise summaries while providing exact source references for every response.
RAG Answer With Citations
Sources attachedUser question
What is the claim procedure for a B2B partner?
3 fragments found
Controlled answer
RAG systems integrated into operational workflows
Private knowledge bases optimize team performance by automating access to procedures and reducing query resolution loops from hours to seconds.
82%
HR & onboarding
Accelerated employee onboarding via instant access to corporate manuals and training repositories.
Shorter training cycles
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B2B support
Automated answer synthesis for complex client technical inquiries derived from internal product specs.
Faster ticket resolution
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Logistics & transport
Elimination of errors across transport procedures and instant auditing of complex Excel data sheets.
Instant verification
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Back-office operations
Streamlined internal data auditing and automated semantic search across scattered legal contracts.
Zero data silos
Inquire about a similar project ›Seamless integration with your existing data stack
Production-ready, scalable architecture. We connect large language models directly to your company’s core infrastructure and file repositories.
Local file integration
Secure mapping of operational documentation from scattered PDFs, Word files, presentations, and local network directories.
SQL data warehouses
Continuous, encrypted pipelines linking relational databases (PostgreSQL, SQL Server, MySQL) for real-time semantic query processing.
Cloud storage sync
Automated, instant data ingestion tracking file updates across corporate SharePoint, Microsoft Teams, Google Drive, and OneDrive repositories.
CRM & ERP ecosystems
Advanced orchestration linking language models directly into core enterprise systems like SAP, Salesforce, HubSpot, and Jira.
Spreadsheet processing
Dedicated analytics layer optimized for error-free context synthesis and mathematical parsing of complex Excel data sheets.
Operator dashboard
A transparent admin console for internal teams to monitor data quality, manage connection nodes, and audit query performance logs.
AI can process your company’s knowledge without you giving up control over the data.
We design RAG environments built on a Zero Trust architecture. You set the rules, manage access, and decide exactly where your data physically resides.
- Model isolation: We strictly separate your corporate sources and knowledge vectors from public LLMs (zero risk of data leakage).
- Full audit trails: We log every employee prompt, generated response, and cited source for comprehensive IT and security reviews.
- GDPR & Compliance: Architecture designed for strict regulatory compliance, offering both On-Premise and Private Cloud deployment options.
- Data masking: The system automatically masks or blocks personally identifiable information (PII) and sensitive data before any processing occurs.
The Foxnet Guarantee: We never use your company's data to train public or external AI models.
RBAC Firewall Scanner
RBAC ONUser profile
Support specialist
Claims procedure
Allowed
Partner price list
Conditional
Board agreements
Blocked
How does a private RAG differ from a standard chatbot?
Public AI models hallucinate answers. Our Enterprise architecture relies exclusively on hard facts and your data.
| System feature | Standard Chatbot | Foxnet Private RAG |
|---|---|---|
| Knowledge source | Answers generically or based on hardcoded rules. | Extracts context exclusively from corporate sources. |
| Reliability & Accuracy | Often lacks sources (high risk of hallucinations). | Includes precise citations and links to original documents. |
| Permissions & Security | Poor understanding of access and data security. | Strictly respects roles, departments, and access rights (RBAC). |
| Ecosystem Integration | Operates as an isolated software island. | Integrates natively with CRM, SQL, SharePoint, and drives. |
| Analytics & Control | Difficult to evaluate and control answer quality. | Features full audit logs, validation, and a feedback loop. |
Knowledge source
Standard Chatbot
Answers generically or based on hardcoded rules.
Foxnet Private RAG
Extracts context exclusively from corporate sources.
Reliability & Accuracy
Standard Chatbot
Often lacks sources (high risk of hallucinations).
Foxnet Private RAG
Includes precise citations and links to original documents.
Permissions & Security
Standard Chatbot
Poor understanding of access and data security.
Foxnet Private RAG
Strictly respects roles, departments, and access rights (RBAC).
Ecosystem Integration
Standard Chatbot
Operates as an isolated software island.
Foxnet Private RAG
Integrates natively with CRM, SQL, SharePoint, and drives.
Analytics & Control
Standard Chatbot
Difficult to evaluate and control answer quality.
Foxnet Private RAG
Features full audit logs, validation, and a feedback loop.
Frequently asked questions
Is our data sent to public AI models?
Absolutely not. The RAG architecture guarantees that your documents are used solely as search context within a strictly closed environment. They are never used to train external or public neural networks.
Will users only see data they are authorized to access?
Yes. We integrate natively with Active Directory and respect Role-Based Access Control (RBAC). If an employee does not have permission to view a specific financial or HR folder, the AI Agent will completely ignore those files when generating their response.
Do our documents need to be perfectly organized before we start?
Not at all. Our vector engine handles various formats seamlessly (PDFs, Word files, scans, emails). We conduct an audit and help map your processes before deployment, so a massive data cleanup is not required to start the project.
What happens if the system cannot find the answer in our sources?
A Private RAG is designed to eliminate hallucinations. If the answer doesn't exist in your documents, the system clearly states that it doesn't know. Simultaneously, administrators receive a notification about the knowledge gap so they can update the internal procedures.
Didn't find the answer to your question?
We start with one knowledge area, not a giant project
The first step does not require rewriting the entire company. The best start is one process, real questions, limited scope, and measurable answer accuracy.
- 01
Source audit
We choose the process, user type, and sources with the highest impact on work time or service quality.
- 02
Architecture design
We define access, updates, citation, logging, and integration with the existing stack.
- 03
RAG pilot
We build the first index, connect sources, and test answers on real team questions.
- 04
Validation and measurement
We check answer accuracy, documentation gaps, handling time, and cases requiring escalation.
- 05
Scaling
We extend the knowledge base with more sources, teams, roles, and AI agent scenarios.
Let’s assess your data potential.
Let’s discuss your internal processes. During a short call, we will evaluate if your documents are ready for a private RAG environment and map out your potential ROI.