Your data becomes your greatest competitive advantage
Documents & Knowledge — RAG as the foundation for all AI
Retrieval Augmented Generation (RAG) is AI’s knowledge base. It is the technology that enables AI to find the right information instantly, answer with sources and never make things up. Without RAG, AI guesses. With RAG, it knows. We help you build a RAG solution that makes your entire organisation’s knowledge searchable, traceable and accessible — whether it resides in documents, websites, databases or spreadsheets.
Some clients & partners
Your organisation has all the knowledge.
But the AI can’t find it.
Knowledge buried in documents
Policies, manuals, reports and product data exist — but are spread across hundreds of folders, systems and file formats. Finding the right answer takes hours instead of seconds.
AI without a source = AI that guesses
General AI tools like ChatGPT and Copilot answer from their training data — not yours. Without access to your documents they cannot provide accurate, business-specific answers.
The same questions, over and over again
Employees, customers and partners ask the same questions — and each time someone has to look up the answer manually. It costs time, creates bottlenecks and burdens key personnel.
What is RAG?
AI’s brain and knowledge database
Retrieval Augmented Generation means the AI does not answer from its own memory — it first searches through your data, finds the most relevant sources and builds the answer based on them. Think of it as the difference between asking someone who guesses and someone who first looks up the answer in your own documents. RAG is the cornerstone that enables semantic search, traceable answers and hallucination-free AI experiences.
RAG is the foundation for everything AI can do in your organisation
Without a solid RAG solution, AI is just a chatbot that guesses. With RAG it becomes a knowledge engine that delivers verified answers, drives semantic search and enables critical agent flows.
Semantic search
Users search with natural language and get exactly the right answer — even if they don’t know the exact document name or keyword. RAG understands intent, not just text.
Hallucination-free answers
Every answer is grounded in your actual data sources with full source references. The AI is not allowed to improvise, guess or fill in gaps — only answer based on verified data.
Critical agent flows
RAG is the cornerstone of all enterprise agent flows. Without reliable data retrieval, agents cannot make decisions, generate reports or automate processes safely.
All your data — one searchable AI layer
RAG works regardless of where your knowledge resides. We connect all your data sources into a single, intelligent knowledge base.
Documents & files
- PDFs, Word documents and PowerPoints
- Excel files, CSV and tabular data
- Internal manuals, policies and handbooks
- Archived reports and investigations
Websites & digital content
- Websites are indexed and kept up to date
- Intranets and knowledge bases
- Product pages and technical documentation
- Help centres and FAQ pages
Systems & databases
- SQL databases and data warehouses
- CRM, ERP and business systems
- APIs and third-party connections
- Product data, PIM and catalogues
Without RAG, AI guesses. With RAG, it knows.
RAG is not a nice-to-have — it is a prerequisite for all organisations that want to use AI for real. Here is why.
Traceable answers with source references
Every answer the AI provides can be traced back to exactly which source it came from — which document, which page, which database. It builds trust and enables quality assurance across the organisation.
Prerequisite for secure AI
Without RAG you have no control over what the AI bases its answers on. With RAG you determine exactly which data sources may be used — and the AI strictly adheres to them.
Scalable knowledge without bottlenecks
Instead of key personnel answering the same questions over and over, the entire organisation’s knowledge becomes accessible to everyone, around the clock. New staff become productive faster and support teams answer questions in seconds.
Foundation for everything that follows
RAG is not an isolated project — it is the foundation on which semantic search, AI assistants, agent flows, product configurators and all other AI applications are built. Start here, and the rest follows naturally.
From mapping to AI-driven knowledge
We always start by understanding your business. Then we build the RAG solution that makes your data accessible.
Mapping & workshop
We start with a workshop where we map your data sources, identify the most valuable knowledge areas and define which use cases to solve first. You get a clear plan.
Week 1Implementation & indexing
We connect your data sources, configure the RAG pipeline and index all data. Our proprietary data processing engine structures, normalises and contextualises the material automatically.
Week 1–2Launch & optimisation
The RAG solution is deployed — either via Noda, custom agent flows or API. We test, optimise and ensure the answers meet the quality you need. Then it scales.
Week 2–3What you can build on a RAG foundation
Once the RAG layer is in place, the doors open to a range of AI applications that all build on verified, traceable data.
Internal knowledge assistant
- Employees chat with all company data
- Answers based on policies, manuals and processes
- Onboarding in days instead of weeks
- Reduces the load on HR and key personnel
Customer support & self-service
- AI that answers customers with accurate product info
- Retrieves documentation and manuals in real time
- Reduces handling time by 25–40%
- Scalable support without additional hires
Agent flows & automation
- RAG as a data source for autonomous agents
- Report generation based on verified data
- Prompt2SQL against your own databases
- Real-time decision briefs for leadership
We build RAG that actually works in production
Many can set up a simple RAG demo. We build solutions that handle enterprise data volumes, respect security and deliver accurate answers at scale.
Proprietary data processing engine
We don’t rely on generic embeddings. Our own engine structures, normalises and contextualises your data for optimal RAG results — whether it is PDFs, web pages or database queries.
State of the art retrieval
We use the latest techniques in vector search, hybrid retrieval and re-ranking to ensure the AI always finds the most relevant sources — not just those that match keywords.
Enterprise volume and security
Our RAG solutions handle hundreds of thousands of documents, respect role and permission controls and run on European servers with end-to-end encryption. Built for production, not demos.
From workshop to production in weeks
We combine consultative mapping with rapid implementation. You don’t just get a plan — you get a working RAG solution that delivers value from day one.
Measurable results from day one
Organisations that implement RAG with Walma see immediate improvements in how knowledge is found, shared and used.
70–90% faster answers
Information that previously took minutes or hours to find is delivered in seconds — with source references and full traceability.
100% traceable answers
Every AI answer can be verified against the original source. No guesses, no hallucinations, no unfounded answers.
+35% faster onboarding
New staff gain access to all organisational knowledge from day one by asking questions in natural language — instead of searching through folders.
RAG with Walma vs without RAG
The difference between AI that guesses and AI that knows — and why it matters for the entire business.
| Funktion | With Walma RAG | ChatGPT / Copilot | Without AI |
|---|---|---|---|
| Answers based on your own data | — | — | |
| Source references in every answer | — | — | |
| Hallucination-free answers | Limited | — | |
| Indexing of documents, websites and databases | Limited | — | |
| Semantic search with natural language | Limited | — | |
| Foundation for agent flows and automation | — | — | |
| Role and permission control per source | Limited | — | |
| European data sovereignty | — | ||
| Unlimited data volume | — | ||
| Real-time update of data sources | — | — | |
| Production-ready from day one | — | — | |
| Time to find the right information | Seconds | Minutes | Hours |
Frequently asked questions about RAG & document management
RAG — Retrieval Augmented Generation — means the AI first searches through your data sources and then builds the answer based on what it finds, with source references. Without RAG, AI answers from its general training data, which often leads to hallucinations and incorrect answers.
Virtually everything. PDFs, Word documents, Excel files, PowerPoints, web pages, SQL databases, APIs, CRM systems, ERP systems, product data, intranets and internal file libraries.
ChatGPT answers from its general training data — it doesn’t know what your organisation’s policies, products or processes are. With RAG you connect the AI to your own data, meaning it answers with business-specific information, always with source references.
Most organisations are up and running within 2–3 weeks. We start with a mapping workshop, connect your data sources, index the material and deploy the solution.
Yes. All data is stored on European servers with end-to-end encryption and isolated customer environments. We offer role and permission control per data source, full audit logs and the option to run the solution on-prem or in a private cloud.
Absolutely — that is exactly how most of our customers do it. RAG is the foundation that agent flows build on. Many customers start with RAG and then expand with agent flows once the foundation is in place.
The RAG solution stays up to date automatically. When new documents are added or existing ones are updated, the changes are indexed in real time or via scheduled synchronisation.