What Problem Does RAG Implementation Solve?
Teams waste 30% of their workday hunting for information across disconnected systems instead of creating value.
The Information Crisis Costs
- $31.5 billion in annual productivity losses (Source: IDC, 2024)
- 2.5 hours per day per knowledge worker searching for information
- Critical decisions delayed while teams hunt for answers
- Institutional knowledge lost when employees leave
Common Scenarios
- Engineers hunt through Confluence for 6-month-old API specifications
- Sales teams can't locate competitive analysis in SharePoint
- Support agents search longer than they spend helping customers
RAG Implementation by STX Next
Enterprise AI Knowledge Engines solve information silos by deploying private AI search systems within company infrastructure.
Key outcomes:
→ 60% reduction in time spent searching for information
→ Complete data security - no external API usage
→ 100% source citations - eliminates AI hallucinations
→ 90-day implementation with measurable ROI

"Traditional search treats business knowledge like generic web content. Our RAG implementations understand your specific context and never hallucinate because they cite sources. We've deployed this for global companies like Linde, turning months of information hunting into seconds of verified answers."
— Tomasz Jędrośka, Head of Data Engineering, STX Next
How Does STX Next Implement RAG for AI Knowledge Systems?
Our 4-phase methodology eliminates risk through incremental deployment and validation.
Phase 1: Knowledge Audit (Weeks 1-2)
- Map current information systems
- Identify highest-impact use cases
- Design security architecture for private deployment
Phase 2: Proof of Concept (Weeks 3-6)
- Build working prototype on limited dataset
- Demonstrate 95%+ accuracy with citations
- Train team and measure improvements
Phase 3: Production Deployment (Months 2-3)
- Integrate major systems (Confluence, SharePoint, Slack)
- Implement role-based access controls
- Scale to full organization with monitoring
Phase 4: Continuous Improvement (Ongoing)
- Monitor usage patterns and accuracy
- Expand to new data sources
- Regular model updates and optimization
What Results Can You Expect from RAG Implementation?
Based on our past projects, you can expect measurable productivity improvements within 90 days, with documented ROI across multiple metrics.
Productivity Gains:
- Information Search Time: 2.5 hours/day → 1 hour/day (60% reduction)
- Question Response Time: 9 minutes → 47 seconds (92% faster)
- New Employee Onboarding: Standard timeline → 40% faster time to productivity
- Project Delivery: Baseline → 11% shorter timelines
Security Features:
- Private cloud deployment in your environment
- Zero external API usage - no data leakage
- Role-based access controls and audit trails
- Compliance ready for GDPR, HIPAA, SOX
Accuracy Guarantee:
- 100% source citations for every answer
- No hallucinations - system says "unknown" when data unavailable
- Verifiable responses with clickable source links
Your data is handled by STX Next S.A., processed to respond to your form requests based on our legitimate interest. You have rights to object to, access, correct, erase, and restrict processing. Find more details in our Privacy Policy.
RAG Implementation FAQ
How do you guarantee data security and prevent external training?
Complete private cloud deployment within your infrastructure. Your data never touches external APIs like OpenAI or Claude. AI models run entirely in your environment, ensuring data sovereignty and zero external usage.
What are the real costs and timeline?
Phased investment structure with validated ROI at each stage.
- Proof of Concept: 4-6 weeks, $25-50K
- Production Deployment: 3-4 months, $100-200K
- Enterprise Rollout: 6-12 months, $300-500K+
How do you prevent AI hallucinations?
Advanced RAG (Retrieval-Augmented Generation) forces source citations. The AI only answers from your documents. If information isn't available, the system explicitly states "unknown." Every response includes clickable source verification.
Will this disrupt our existing workflows?
API-first integration works with current systems without workflow changes. We've integrated with legacy SharePoint, modern cloud databases, and everything between. Your teams continue using familiar tools.
How do you handle enterprise scale across departments?
Containerized microservices with multi-tenant architecture. Horizontal scaling supports different access controls and data sources for various business units. Designed for enterprise scale from day one.
How do we measure and prove ROI?
Comprehensive analytics track quantifiable productivity metrics. Monitor search time reduction, query resolution rates, user adoption. Typical results: 20-25% productivity improvement within 90 days.
What if we want to bring this in-house later?
Complete code ownership with open-source foundations. You own all code and documentation. Built on standard Python frameworks - any qualified development team can maintain and evolve the system.

Don’t just take our word for it:




Get A Risk-Free Knowledge Audit
A comprehensive analysis you keep regardless of proceeding, including exact productivity calculations and implementation roadmap.
- Productivity Loss Assessment
- Exact time and money calculations
- Department-by-department analysis
- Knowledge Gap Analysis
- Critical missing information identified
- Hard-to-find content mapped
- Custom Implementation Roadmap
- Specific timeline for your environment
- Resource requirements detailed
- ROI Projections
- Financial impact model using your data
- Break-even timeline calculations
100% Value Guarantee
You retain the complete analysis even if you don't proceed with implementation.
Get Started with RAG Implementation
Schedule a 30-minute technical discovery call and your free knowledge audit.
Your data is handled by STX Next S.A., processed to respond to your form requests based on our legitimate interest. You have rights to object to, access, correct, erase, and restrict processing. Find more details in our Privacy Policy.