We integrate LLM models and RAG systems into the corporate software of Barcelona companies to create intelligent internal assistants on their own documentation and systems. Local team available on-site for architecture design phases and validation with your organization's teams.
AI Integration in Corporate Software with LLM and RAG in Barcelona
At MiT Software we specialize in integrating LLM models and RAG systems into the corporate software of Barcelona companies. Our local team understands the needs of Barcelona's business ecosystem — growing startups, scale-ups and corporations with agile structures — and designs solutions that allow your teams to access the organization's internal knowledge using natural language, with precise and verifiable responses. We work on your existing infrastructure without replacing it, adding an intelligence layer that transforms how your teams work with your company's knowledge and data.
We start with a comprehensive analysis of existing knowledge sources: what documents exist, in what systems they are stored, who uses them and what queries they try to answer. This analysis defines the RAG system architecture.
We define the complete technical architecture: which LLM model is most suitable, how to structure the vector index for maximum retrieval precision, what chunking strategy to apply and how to manage real-time document base updates.
We build the processing pipeline that transforms your documents into semantic vectors, indexes them in a vector database and retrieves them precisely for each user query. The pipeline integrates with your existing storage systems.
We connect the RAG system to your work tools via certified APIs: SharePoint, Confluence, Google Drive, Salesforce, SAP or any document or enterprise management system. The integration is transparent for the end user.
We evaluate the system with real questions from teams' daily work, measuring response accuracy, document relevance, response time under load and access control policy compliance before production deployment.
We deploy the system on corporate infrastructure and train teams on its use. After launch, we monitor response quality, identify knowledge gaps and improve the system iteratively with each review cycle.
With a well-implemented RAG system, any employee can ask complex questions about the organization's internal knowledge and receive precise, contextualized responses with references to original sources in seconds. No more searching through folders or reading entire documents to find a specific piece of information.
Unlike generic language models, the RAG systems we develop respond based exclusively on your company's real knowledge, updated in real time. Every response includes the source so the user can verify it, and the system never invents data that does not exist in your document base.
We build RAG pipelines that index your corporate documentation — technical manuals, procedures, contracts, reports, policies — and allow employees to query it using natural language with precise responses and references to source documents.
We connect language models with your enterprise management systems to create assistants that can query real-time data, generate automatic reports, draft communications and execute simple actions through natural language instructions.
We develop function-specific assistants: the legal assistant that reviews contracts, the HR assistant that answers policy questions, the financial assistant that generates reports and analyses budget deviations. Each assistant masters the language and context of its department.
We build automated pipelines that process large volumes of documents — invoices, contracts, reports, forms — extracting structured information, classifying by category, detecting anomalies and feeding management systems automatically.
We implement RAG systems with granular access control ensuring each user can only query information they are permitted to access. The security architecture complies with GDPR and corporate security standards, with an audit log of every query made.
For use cases requiring superior specialization, we perform fine-tuning of foundation models on your company's proprietary data, creating models that master the language, context and nuances of your sector with significantly superior accuracy.
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