Generic AI answers from the open internet. We build knowledge systems that answer from your specific documents, databases, and institutional knowledge.
Retrieval-augmented generation (RAG) is a method that lets an AI model answer questions from a business's own documents and data instead of only from its training data: the documents are split, converted to embeddings and stored in a vector database, and the most relevant passages are retrieved and given to the model with each question. A knowledge base built this way stays current when the documents change.
GrossiWeb is a web design and digital marketing agency founded in 2006 and based in Atlanta, Georgia, serving Raleigh, Los Angeles and businesses nationwide. Its knowledge and data systems service builds complete RAG systems, from document ingestion and vector storage to retrieval tuning and a chat or API interface, and is quoted after a scoping call.
RAG (Retrieval Augmented Generation) allows AI to answer questions using your specific documents and data: not just its training data. We build complete RAG systems: document ingestion, embedding, vector storage, and retrieval: connected to an LLM front-end your team can actually use.
Document ingestion pipeline, chunking strategy, embedding model selection, and vector database setup.
Semantic search with hybrid retrieval, metadata filtering, and relevance tuning.
Chat interface, API endpoint, or integration into your existing tools: however your team needs to access it.
18%
of US firms used AI in a business function, Nov 2025 to Jan 2026; 32% when weighted by employment
58%
of US small businesses say they use generative AI, up from 40% in 2024 and 23% in 2023
88%
of organizations surveyed use AI in at least one business function, up from 78% in 2024
5%
of custom enterprise AI tools reach production; most stall on brittle workflows and tools that don't learn
The knowledge base in RAG is the collection of a business's own documents, records and data that the system retrieves from: policies, manuals, contracts, support tickets, product data or database rows. GrossiWeb builds the pipeline that ingests those sources, chooses the chunking strategy and embedding model, and stores the result in a vector database with metadata so retrieval can be filtered by document type, date or customer.
In business, a RAG system is an internal or customer-facing assistant that answers from company knowledge with citations to the source document, rather than guessing from general training data. Typical uses are staff answering policy and procedure questions, support teams finding past resolutions, and customers asking about products. GrossiWeb delivers it as a chat interface, an API endpoint or an integration into the tools a team already uses.
GrossiWeb quotes knowledge systems after a scoping call and does not publish a starting price, because the number and format of source documents, the retrieval quality required and the interface decide the work. Vector database, embedding and model usage fees are billed by the vendors to the client and estimated from a pilot run on a sample of the documents before the full build is quoted.
Yes. Larger model context windows have not removed the need to retrieve the right passages from thousands of documents, keep answers current when documents change, cite sources and control cost per question. GrossiWeb builds retrieval with hybrid search and relevance tuning for those reasons. Agents that act on the retrieved knowledge are built under custom AI agents; production features go through AI builds.
Internal examples are an HR and policy assistant, a sales assistant that answers from past proposals, and a support assistant that searches previous tickets; customer-facing examples are product and documentation assistants on a website. Firms with heavy document loads, such as legal and law firms and construction and home services contractors with specifications and permits, are common cases.
A system that lets staff or customers ask questions and receive answers drawn from the company's own documents, with the source shown, instead of general answers from the open internet. GrossiWeb builds the ingestion, retrieval and interface, and connects it to other systems through LLM and MCP integrations. Businesses deciding where to begin can use the automation and scale solution.
AI & Automation work rarely stands alone. These are the services we most often pair with this one.