Advanced RAG Systems
Answers grounded in your data, with sources
Retrieval-Augmented Generation for always-up-to-date business knowledge with semantic search and contextual generation.
Can a B2B customer return a product after 3 weeks?
- [1]Support_manual.pdf · §4.2
Returns are free within 30 days if the product is intact…
- [2]Returns_policy_2026.docx
B2B returns must be authorised from the customer area…
- [3]Internal FAQ · Wiki
Return labels are generated from the orders panel…
Yes: returns are free within 30 days if the product is intact [1]. For B2B customers it must first be authorised in the customer area [2].
01Capabilities
Company knowledge, always up to date
A language model that first searches your documents and then answers, citing where each piece of information comes from.
Semantic search
Contextual, relevant information, even across very large data sets.
Contextual generation
Answers built on your company’s specific knowledge.
Real-time updates
Knowledge kept in sync with your data sources, without retraining the model.
Access control
Permissions and security for sensitive information: everyone sees only what they are allowed to.
02How RAG works
Retrieval and generation, in one flow
Retrieval-Augmented Generation combines search over your data with an LLM’s text generation.
Knowledge base
Documents, manuals, procedures and business data are processed and indexed in a vector database.
Retrieval
When a question arrives, the system finds the most relevant passages using semantic search.
Augmentation
Retrieved passages are combined with the question to build a rich, specific context.
Generation
The LLM generates the answer from your real data, with source citations.
03Comparison
RAG or LLM only?
Why business applications should connect the model to your own knowledge.
Traditional AI · LLM only
- Knowledge limited to training data
- Risk of hallucination
- No verifiable sources
- Data cannot be updated
- Costly fine-tuning
RAG system
- Access to your knowledge base
- Answers grounded in real facts
- Citations and sources included
- Real-time updates
- No fine-tuning needed
04Use cases
How access to knowledge changes
Scenarios where a RAG system answers instead of a manual search.
Enterprise knowledge base
Immediate answers on internal procedures, policies, manuals and technical documentation.
Product support
Technical support based on manuals, FAQs and ticket history, with references to the official documentation.
Legal analysis
Search across contracts, regulations and precedents, with extraction of relevant clauses and comparative analysis.
Scientific research
Analysis of papers, patents and publications, with summaries and links between studies.
Training & Onboarding
Interactive training for new employees based on company materials.
Business Intelligence
Query reports, analytics and historical data in natural language: insights for everyone, not just analysts.
05What you get
A tailored RAG, ready for production
From the indexing pipeline to the APIs, all the way to usage metrics.
Included features
- Customized Retrieval-Augmented Generation
- Semantic search on business documents
- Intelligent contextual generation
- Automatic knowledge updates
- API for integration with existing systems
- Usage analytics and metrics
Benefits
- Always up-to-date, verifiable answers
- Less time spent searching for information
- Decisions made on better data
- Company knowledge accessible to the whole team
06Stack
The RAG stack
Components chosen based on data volume, privacy requirements and budget.
- Vector databases
- Pinecone, Weaviate, Qdrant, Chroma
- Embedding models
- OpenAI, Cohere, BGE, Sentence Transformers
- LLM generation
- GPT, Claude, Llama, Mixtral
- Orchestration
- LangChain, LlamaIndex, Haystack, custom pipeline
07FAQ
Frequently asked questions
Have a specific case? Tell us about it.
Talk to an expert08More solutions
They work together
A chatbot can sit on top of a RAG system, which in turn reads your document archive. We design the pieces to work together.
AI Chatbots & Support
Intelligent customer support systems with advanced NLP, multi-channel integration and continuous learning.
Smart File Management
AI for automatic organization, semantic search and intelligent processing of business documents.
Multi-Agent Orchestration
Complex systems with specialized AI agents that collaborate to automate processes and intelligent decision-making.
Next step
Let your company’s knowledge speak
With a RAG system your team always has up-to-date, contextual information, with the sources one click away.
