How do I train AI on my company's documents using RAG?

RAG (Retrieval-Augmented Generation) lets AI answer using your private documents without retraining the model. AISDC ingests your PDFs, manuals, Notion and wikis, splits them into chunks, and turns them into embeddings stored in a vector database; when someone asks, it retrieves the most relevant passages and the AI answers grounded in your real sources, avoiding hallucinations.

Which vector database should my company in Mexico use?

For most cases under 10 million documents, AISDC recommends Pgvector, the Postgres extension: a single, transactional, easy-to-operate database. For larger scale or specialized needs we use Zilliz/Milvus, Pinecone, Weaviate, Qdrant, or Vertex AI Vector Search, chosen by your volume, budget, and infrastructure.

How long does it take to deploy a RAG knowledge-base chatbot?

For most SMEs in Mexico a RAG system launches in weeks, not months, because it requires no model retraining: you just upload new documents to update the knowledge. AISDC ingests your sources, vectorizes them with the right strategy, and builds a query layer with reranking, citations, and access control, with real-time or scheduled updates.

RAG & Vector Databases for Business

What Is RAG and a Vector Database?

RAG (Retrieval-Augmented Generation) is an architecture that bridges two capabilities: the reasoning power of a large language model and the precise knowledge stored in your own documents. Rather than relying solely on what a model learned during training, RAG performs real-time lookups against a vector database—a system that stores text converted into embeddings, which are numerical representations of semantic meaning—to surface the most relevant fragments before generating a response. Technologies such as Milvus and Zilliz Cloud enable high-precision semantic search at enterprise scale, allowing any organization to connect an LLM directly to its internal knowledge base without retraining the model or exposing sensitive data to third parties.

Why It Matters: an LLM That Answers With YOUR Data Without Hallucinating

General-purpose LLMs carry a critical limitation: their knowledge is frozen in time and alien to your business. RAG solves this by grounding every response in fragments retrieved from your own documents, manuals, contracts, or knowledge bases. The model does not invent; it cites. This drastically reduces hallucinations—plausible but incorrect answers—because the injected context acts as a verifiable source of truth. Multilingual embeddings allow the same pipeline to handle queries in Spanish, English, or other languages without parallel systems. For companies in Monterrey or with global operations, this means a technical, legal, or commercial assistant that speaks with the precision of your internal documentation and the natural fluency of a state-of-the-art language model.

Use Cases: Proprietary Knowledge, Semantic Search, Support, and Analysis

RAG implementations combining semantic search and vector databases address a broad range of real business needs. Knowledge-grounded assistants answer questions about internal policies, product catalogs, or technical documentation directly from company files. Semantic document search retrieves contracts, records, or reports even when the user cannot recall the exact words—only the concept. In customer support, a RAG agent reduces escalations by resolving queries accurately using actual product manuals. In analysis, it enables non-technical teams to ask natural-language questions over large collections of reports or qualitative data. All these use cases share the same infrastructure: embeddings indexed in Milvus or Zilliz and semantic retrieval performed before every generation step.

How We Build It: From Ingestion to Production Evaluation

Our production RAG pipeline begins with intelligent ingestion and chunking: we split documents into overlapping fragments sized to preserve context without overwhelming the model's window. We generate embeddings using multilingual models selected for the target domain—code, legal text, or technical support—and index them in Milvus or Zilliz Cloud, tuning index type, distance metric, and retrieval parameters to meet latency and precision requirements. We implement hybrid retrieval strategies—vector plus lexical—for queries where semantic search alone falls short. The complete system is then evaluated with fidelity, relevance, and coverage metrics before deployment, and monitored in production to detect drift in response quality over time. The result is a RAG system that is robust, observable, and maintainable for the long term.

RAG & Vector Databases

RAG System Development

Data Inventory & Strategy

Pipeline & Index Build

Search Tuning & Evaluation

Production Deployment

What We Deliver

High-Dimensional Embeddings

Hybrid Search (Sparse + Dense)

Zilliz/Milvus Vector Store

Document Ingestion Pipeline

Context Window Optimization

Grounding & Citation

RAG in Action

Enterprise Knowledge Base

Legal Document Search

Product Catalog Intelligence

Enterprise Knowledge Base

Legal Document Search

Product Catalog Intelligence

Industries we serve with this

Frequently asked questions

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Related Services

What Is RAG and a Vector Database?

Why It Matters: an LLM That Answers With YOUR Data Without Hallucinating

Use Cases: Proprietary Knowledge, Semantic Search, Support, and Analysis

How We Build It: From Ingestion to Production Evaluation