Service
RAG -
Retrieval Augmented Generation
Your employees ask questions and get answers from your own data. Your processes access internal knowledge automatically. No hallucinating, no guessing, no generic answers. We plan, build, test and deploy RAG systems that make your company knowledge productively usable – whether in dialogue with staff or as the knowledge layer for your automation.
Trusted by over 120 companies
The Reality
Your company has the knowledge. But nobody can find it.
Manuals, policies, contracts, technical documentation, process descriptions, it’s all there. Spread across SharePoint, Confluence, file servers, SAP, and 47 PDFs on the team lead’s desktop.
Searching instead of working
Employees spend up to 20% of their working time searching for information. Processes stall because data sits in silos. Internal search returns nothing relevant, so people ask a colleague or dig manually through folders, systems and emails.
ChatGPT is not the answer
Generic LLMs hallucinate, have no access to your internal data and breach data protection policies. Whether an employee asks a question or a process needs information: the answer must come from your documents and systems, not from the internet.
Fine-tuning is too expensive and too rigid
Training your own LLM costs months and hundreds of thousands. And when a document changes? Retrain. RAG solves this: current data, no retraining, immediately available.
How We Solve It
From data source to productive
AI knowledge base
We build RAG systems end-to-end: connect data sources,
process documents, build a vector database, optimise
retrieval, and ensure answer quality. In production in 4–8 weeks.
Connect Data Sources
SharePoint, Confluence, file servers, SAP, databases, websites, we connect all relevant knowledge sources. Automatically synchronized, incrementally updated, permissions respected.
Chunking & Embedding
Documents are intelligently split, semantically understood, and stored as vectors. Not brute-force slicing, but context-aware chunking that determines answer quality.
Retrieval & Response
Find the right information and only the right information. Hybrid search (semantic + keyword), re-ranking, source citation with every answer. No hallucinations, no guessing.
The Difference
RAG vs. Fine-Tuning vs. Generic LLM
| Generic LLM ChatGPT & Co. | Fine-Tuning Custom Model | RAG with Lunatec | |
|---|---|---|---|
| Your Data | Doesn't know it | Baked into the model | Retrieved live from your sources |
| Currency | Training data cutoff | As of last training run | Always current Data retrieved live |
| Hallucinations | Frequent Invents plausible-sounding answers | Less frequent, but possible | Minimal Every answer with source citation |
| Data Privacy | Data leaves your organization | Can be internal | 100% in your infrastructure Azure, on-prem, or hybrid |
| Cost | Low (API costs) | €100K+ for training + infrastructure | €20–50K for a production system |
| Time-to-Value | Instant (but unusable for internal data) | 3–6 months | 4–8 weeks to production |
| When Data Changes | No update possible | Retrain (weeks, €€€) | Automatic New document gets indexed |
In Practice
Same question - two worlds
A customer service agent asks: “What goodwill policy applies for Product X for customers with framework agreement Type B?”
- Agent opens SharePoint
- Searches for "goodwill policy" — 47 results
- Opens 5 PDFs, none quite right
- Asks team lead — who's in a meeting
- Emails product management
- Waits 2 days for a response
- Answers customer inquiry — hopefully correctly
- Agent opens RAG interface
- Types: "goodwill policy Product X, framework agreement Type B"
- RAG searches 12,000 documents in 2 seconds
- Finds relevant passage in Goodwill Policy v3.2
- Generates precise answer with source citation
- Agent clicks source link — verifies
- Answers customer inquiry — correctly and immediately
Use Cases
Where RAG Has the Greatest Impact
RAG is valuable wherever people regularly need information from large document repositories: fast, accurate, and traceable.
Internal Knowledge Management
Policies, manuals, SOPs, process documentation – one question, one answer with source. For all employees, 24/7, without needing to ask the expert.
−80% search time, All departments
Customer Service & Support
Agent queries RAG instead of 5 systems. Product info, warranty terms, goodwill rules, technical specs, instant, accurate answers for the customer.
−60% handling time, Consistent answers
Contract & Compliance Analysis
“Which contracts have a notice period under 3 months?” RAG searches thousands of contracts and delivers the answer with source citation.
Minutes instead of daysLegal & Compliance
Technical Documentation
Assembly instructions, maintenance manuals, error codes: technicians query the RAG system in the field and get the exact answer with a reference to the right page in the manual.
Field ServiceManufacturing
Onboarding & Training
New employees ask everything they need to know and get answers from the actual knowledge base, not from outdated training materials.
−50% onboarding time HR
Regulatory Knowledge
“What does the regulator say about topic X?” – RAG searches all regulatory documents, circulars, and internal interpretations and delivers the current answer.
Our Approach
From idea to production RAG system in 4 phases
Not a 6-month research project. A pragmatic approach with measurable results after every phase.
01
Discovery & Data Analysis
Which data sources? Which formats? What questions do your employees ask most frequently? We analyze your knowledge base and define the MVP scope. 1 week.
02
RAG Architecture & Build
Connect data sources, define chunking strategy, select embedding model, set up vector database, build retrieval pipeline. Architecture decisions that determine quality. 2–3 weeks.
04
Quality Assurance & Testing
Systematic testing with real questions from your employees. Measure retrieval quality, evaluate answer accuracy, identify edge cases. Iterate until quality is right. 1–2 weeks.
05
Go-Live & Operations
Roll out to pilot group, set up feedback loop, deploy monitoring, implement access controls. Then: scale. Managed services for ongoing operations and optimisation. 1 week.
Typical Results
What Our Clients Achieve with RAG
(source-cited)
12,000 documents
Technology
The Platform Behind Our RAG Systems
We don’t build toys – we build production systems with enterprise security, access controls, and scalability.
Ready?
How long do your employees spend
searching for the right information?
Let’s look together in 30 minutes at which processes have the greatest RAG leverage.
No sales pitch. Just an honest assessment.
120+ Clients. 100% Satisfaction. 7 months to Profitability.
WHAT YOU GET IN THE DISCOVERY CALL
Identify your best use cases
Based on your industry and process landscape
Calculate concrete ROI
In Euros, FTE equivalents and time savings
Show examples from your industry
Real results of comparable companies
Define timeline and next steps
Concrete roadmap, no vague promises