Prepare and index your knowledge
We organise document and data ingestion and updates. Embeddings represent their content to support enterprise semantic search beyond exact keyword matches.
Explore our Artificial Intelligence solutions for business
Enterprise RAG solutions
Connect your business knowledge with the people who need it. We build assistants that search your documents and systems and use that information to answer questions in natural language.
What is enterprise RAG?
RAG (Retrieval-Augmented Generation) is a way to use language models with your company information. Before answering, the system searches your documents and systems for the most relevant passages and gives them to the model as context.
Unlike a generic chatbot, an enterprise RAG system answers from your own sources, can show where each answer comes from and stays up to date when documentation changes, without retraining the model. Artificial Intelligence works with the real knowledge of your organisation.
Your knowledge exists. Finding it takes work.
Scattered documents, difficult searches, repeated questions and disconnected data leave people relying on knowing where to look or whom to ask. Enterprise RAG retrieves relevant context for each query.
RAG development connected to your business
We build AI-powered knowledge bases as part of an integrated solution, from preparing your sources to delivering the interface your team uses.
Retrieve context and answer questions
We retrieve relevant information and provide it to the language model as context for its answers. We define how to display sources and what to do when the available information is insufficient.
Integrate and maintain the solution
We integrate RAG with enterprise systems through APIs and connectors, build the web or mobile interface, and support ongoing maintenance and improvements.
Models, data and platforms
We work with models from different providers, such as Amazon Bedrock on AWS or OpenAI, and choose according to each project: where data must reside, the answer quality required, query volume and integration with existing infrastructure.
We design the whole architecture: source ingestion and updates, embeddings and semantic search, context retrieval, the language model and the interface, deployed in the cloud and connected to your corporate systems.
Where RAG can add value
These are potential use cases to assess against your data, permissions and processes. They are not a list of features delivered for Grupo Noria.
Internal knowledge assistants
Look up internal procedures and guidance through a conversational interface to support everyday work.
Documentation search
Find relevant information in manuals, technical specifications or product documentation without knowing its exact location.
Support and operations
Retrieve instructions and context to handle recurring questions or prepare to resolve an issue.
Information in context
Combine document searches with information from enterprise systems that each user is authorised to access.
Security and integration built into the design
We assess who can access each source, which data can leave each system and what traceability the solution needs. Permissions, authentication and connector boundaries are part of design and validation.
Five Flames has been ISO/IEC 27001 certified since 2020. This framework supports our approach to information security management; it does not certify each RAG solution. We validate answers and their limitations using real queries.
REAL PROJECT · GRUPO NORIA / NNEXT
RAG within an enterprise application
NNext lets Grupo Noria employees and customers access product information in natural language. It combines a knowledge base, RAG and embeddings with task-specific agents, SAP and Jira integration, and native iOS and Android apps.
From a focused use case to an evolving solution
Assess data and systems
We review sources, information quality, permissions and required integrations.
Scope a use case
We define who will use it, which questions it should answer and how to evaluate the responses.
Build and integrate
We connect the sources, information retrieval, model and interface.
Validate with real queries
We check relevance, answers, references and enforcement of permissions.
Deploy and improve
We support production deployment, knowledge updates and ongoing improvements.
Enterprise RAG FAQ
How is a RAG solution different from a generic chatbot?
A generic chatbot answers with what the model learned. A RAG solution first searches your company sources and answers from that information, so answers are grounded in your documents and data and can cite their origin.
What information can an enterprise RAG use?
Documents, manuals, technical sheets, procedures and knowledge bases, as well as information from corporate systems available through APIs. Which sources are included depends on the use case and on each user's permissions.
How are data and permissions protected?
From the design stage we define who can access each source, integrate corporate authentication and check that every user only receives information they are allowed to see. Our information security management system is ISO/IEC 27001 certified.
Which AI models do you use?
We work with several providers, including Amazon Bedrock and OpenAI. We choose the model according to each project's data, answer quality and infrastructure requirements.
How is answer quality validated?
We validate the solution with real queries from the team that will use it: we check answer relevance, source references, behaviour when information is missing and respect for permissions.
Do you build RAG solutions for companies outside Spain?
Yes. We are based in Valladolid, Spain, and build RAG solutions for businesses and public organisations across Spain as well as for international projects.
What knowledge does your team need to find?
Tell us about the information you use and the systems you work with. We can help you define a first RAG use case.