AI with your company data: RAG and knowledge base.
RAG is the technique that makes AI search your company’s documents for answers before responding, always citing the source. We use it to build an AI-powered knowledge base from manuals, contracts, tickets, and internal systems, where each person only accesses what they’re allowed to see.
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Chat on WhatsApp nowRAG applications in daily operations.
When knowledge is scattered across documents and systems, an assistant finds the answer and shows its source.
Customer service
Answers questions, checks orders, and opens tickets, 24/7.
Sales
Qualifies leads, answers product questions, and schedules meetings with sales.
Internal operations
Finds information, fills out forms, and runs routines across systems.
Team support
An assistant that knows your internal procedures and documents.
RAG use cases
Workflows where AI checks your company’s documents and systems before taking action. If a task depends on finding internal information, an assistant can probably help.
Product recommendations
Recommends products based on customer profile, rules, and history, consulting catalogs and contracts. Suggests options for sales, restocking, or support, always aligned with company policies.
Customer support
Checks manuals, contracts, and history to answer questions and guide the client. Reduces tickets and standardizes responses.
Document processing
Reads invoices, contracts, résumés, and receipts, extracts data, fills out systems, and flags anything out of standard.
Financial queries
Checks statements, histories, invoices, and contracts to answer questions, verify payments, or validate information. Automates searches that used to require navigating multiple systems and documents.
Compliance
Checks policies, regulations, and records to answer about rules and obligations. Identifies deviations, documents decisions, and ensures compliance without manual searches.
Any scattered knowledge
If the answer exists in a manual, contract, spreadsheet, or system, but someone has to stop to look for it, AI finds it, responds, and shows the source.
Tell us where yours is
How RAG works.
RAG, short for retrieval-augmented generation, makes AI search your company’s documents and systems for relevant excerpts before answering. This way, the response uses internal content, references the source, and reduces the risk of made-up answers.
From documents to answers.
The assistant only goes live after answering real team questions and passing quality checks. The base stays updated as documents and connected systems change.
- 01
Sources
Which documents, systems, and permissions will make up the knowledge base.
- 02
Ingestion
Documents and data are extracted, broken into smaller parts, and indexed for semantic search.
- 03
Prototype
The team asks real questions and tests the assistant’s answers, adjusting until they meet the expected standard.
- 04
Evaluation
Measures answer quality, always showing where the information came from.
- 05
Production
The chat or integration goes live, already connected to sources and with automatic base updates.
What the project delivers.
- Ingestion and updating of documents and data sources
- Semantic search with permission control
- Chat interface or integration with your existing system
- Answers with source citation
- Answer quality evaluation
Frequently asked questions
Do documents leave the company?
Data never leaves your company. The architecture uses APIs without retention, private cloud, or models installed on the client’s infrastructure, according to required privacy policies.
What if a document changes?
When a document changes, the base updates itself automatically from the connected sources.
Can AI make up answers?
Any language model can make up answers, but the assistant only uses excerpts found in documents and shows their source. If it can’t find the information, it lets you know it wasn’t found in the company files.
Can everyone see everything?
No. The assistant only shows information from documents and systems the user already has permission to access. Answers follow the same access rules as the connected sources.
Which document formats are supported?
PDF, Word, Excel, internal pages, tickets, and system data go in directly. Other formats require evaluation.
What is RAG?
RAG: Retrieval-Augmented Generation means Retrieval-Augmented Generation. The AI model searches your company’s documents and systems for relevant excerpts to answer, instead of relying only on what it “learned” before. Here’s how it works: when it receives a question, the system looks for relevant excerpts in internal files and builds the answer based on that data, citing the source. Without RAG, AI answers only with what was already in the model, without access to the client’s manual or contract.
Can it be used inside an existing system?
Yes. The search can be added to your current system through an LLM integration or as part of an AI-powered system built for this purpose.
How much does it cost and how long does it take?
The price depends on the volume and type of documents, connected systems, and number of users. The proposal details the project cost and a monthly usage estimate.
Where can we implement RAG?
Talk to us to map where RAG can help your operation.