RAG makes a language model answer from your company’s documents, citing the document and the page. Here we explain when it fits, what to ask of a solution and where to start.
RAG stands for Retrieval Augmented Generation. It connects a language model to the company’s knowledge base: the model answers from internal documents and data and cites its sources. Invented answers drop, and the data stays under your control.
Extraction and indexing
PDF, images, Word, Excel, PowerPoint and web pages become text split into smaller parts, indexed with a reference to the source page.
Words and meaning
A hybrid search, lexical and semantic, finds the relevant passages even when the question uses words other than the document’s.
An answer from the retrieved context
The model writes the answer only from the passages it found and cites the document and the page, with a link to the original.
A stated limit
If the information is not in the documents, the system says so and points to the right contact.
When the answer already exists, written down somewhere in the company’s documents.
Manuals, data sheets and procedures already hold the answers. The people at the counter, on site or on the phone have no time to look for them.
Readers must be able to check. RAG cites the document and the page and opens the original in one click.
Pages and documents are indexed again when they change, and the answers follow the updated version.
Students, customers or citizens ask in natural language on the public website, and the answer comes from the official content.
A model can write and reason, but it does not know your company’s manuals, procedures and contracts. On those topics it answers with what it learned elsewhere, and an invented answer can look true.
Meanwhile every department tries its own AI tool: data leaving with no rules and costs nobody sees.
RAG gives the model the right documents for each question, within the rules the company sets.
Before you choose, ask to see these points on a system already in use. They apply to us too.
Each business unit decides what goes into its own knowledge base and who can access it. Search returns only the content the person asking is allowed to see.
Every answer names the document and the page, with a link to the original. If the information is not there, the system says so and points to the right contact.
A single proxy to the model providers: the model is chosen per use case, for example Gemini, Claude, OpenAI or Mistral, and changed from the interface.
Services and inference in European Union regions, personal data masked in the logs, GDPR roles written into the contract.
Open source components, code and configuration under version control, infrastructure as code: the system stays yours even if you change supplier.
Requests, tokens and cost visible per business unit, with a daily, weekly or monthly budget and an automatic alert at 80%.
Test cases taken from real questions, with agreed thresholds, run before every release. The questions left unanswered show which documents are missing.
A knowledge base per business unit, with an AI assistant that answers from internal documents and cites the source. The same components run Luiss’s chatbot with guardrails and its conversational search.
One solution for internal documents, one for search on the website, and the services that build and run them.
People ask in natural language and get answers from the company’s documents, with the source cited. Each business unit has its own rules and budget.
People type a question and find the passage of the page that holds the answer, even when they use words other than the site’s.
We design RAG, agents and guardrails and connect them to your documents and applications. It starts with a workshop and a prototype on your data, with the measures agreed beforehand.
We run AI assistants and agents after release: each one has a person accountable for it, is evaluated before every release and keeps a record ready for audit.
RAG, documents, models, data and costs.
RAG (Retrieval Augmented Generation) connects a language model to the company’s knowledge base. The model answers from internal documents and data, cites its sources and reduces invented answers, while the data stays under your control. It serves internal assistants, customer support and knowledge management.
In two steps. First the system extracts and indexes the documents, and for each question it retrieves the relevant passages with a search by words and by meaning. Then the model builds the answer from that context only and cites the document and the page.
A chatbot is an interface. RAG is how the system behind it finds information in your documents and answers from there. In the AI Knowledge Platform the assistant is one way of using the knowledge base, with model governance, budgets, guardrails, metrics and logs.
Semantic search finds the right content by meaning and returns the passage that answers, with a link to the page. RAG uses that search to retrieve the context, then a model writes the answer and cites the source.
The model answers only from the context retrieved from the indexed content, citing the document and the page. If the information is not there, it returns a courtesy answer that points to the right contact. Guardrails filter harmful content and manipulation.
PDF, images, Word documents, Excel sheets, PowerPoint presentations and web pages. Extraction is tuned on the most used document templates, and each team uploads its own documents.
The right one for the use case, for example Gemini, Claude, OpenAI, Mistral or hosted models. With a single proxy to the providers, each business unit can have its own model and change it from the interface.
In the AI Knowledge Platform, services and inference stay in European Union regions, consistent with the AI Act, and personal data is masked in the logs. The client remains the data controller; SparkFabrik is the data processor under the GDPR.
An agent can use RAG as a tool: it decides whether to query the knowledge base, retrieves the context and builds the answer. To act on other systems it connects through MCP servers, with permissions and tracing: we explain it in AI enterprise solutions: agents and MCP.
We do not publish a price list for the AI Knowledge Platform. The offer always separates three items: the one-off build, the running fee and the pay-per-use model cost, with a budget per business unit.
From one use case with a clear perimeter: a department, its documents and the questions it gets most often. Then the platform grows, department after department, with the same rules.
Bring us a handful of real documents and the questions you get most often: we show you how it answers, with which sources, and what it says when it does not know.
Book a demoCases, articles, talks and guides on the same topic.
Tell us which departments, which documents and which constraints you have. We will tell you which use case we would start from.
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