AI CONNECTED TO YOUR KNOWLEDGE
Enterprise RAG with grounded answers, sources, and access control.
We build RAG systems that retrieve authorized knowledge and supply reliable context for answers, agents, and enterprise AI applications.
DIRECT ANSWER
What is enterprise rag: consulting & rollout?
RAG, or retrieval augmented generation, combines search over external sources with language models to produce answers that are more current, more specific, and grounded.
Who it is forCompanies that want to connect AI to documents, policies, products, tickets, contracts, technical libraries, or other internal repositories.
CONTEXT
Technical decisions with an operational view.
A dependable RAG starts with the quality of the sources and of the retrieval. Loading documents into a vector database does not guarantee the right passage reaches the model.
We design ingestion, chunking, metadata, hybrid search, reranking, permissions, and citations according to the type of content and the user's question.
The solution ships with an evaluation suite that measures retrieval, grounding, answer quality, latency, and cost, which makes continuous improvement objective.
OUTCOMES
What the initiative has to deliver.
Technical goals only matter when they improve security, speed, cost, experience or the ability to decide.
- 01Answers tied to their sources
- 02Searchable corporate knowledge
- 03Permissions preserved
- 04Fewer ungrounded answers
- 05Reproducible evaluation
- 06Integration with agents and systems
WHEN IT MAKES SENSE
Signs that it is time to act.
- AI has to answer questions about internal data
- Documents change frequently
- Answers have to cite sources
- The current search finds too little
- Content has different access levels
- The RAG prototype is not consistently good
HOW WE WORK
From assessment to operations.
Short stages, visible criteria and knowledge transfer at every decision.
Knowledge
We map sources, users, permissions, and questions.
Retrieval
We test chunking, indexes, search, and reranking.
Answer
We build prompts, citations, guardrails, and integration.
Evaluation
We measure quality, cost, latency, and failures by scenario.
DELIVERABLES
Clarity on what gets finished.
- RAG architecture
- Ingestion pipeline
- Index and search
- Answer API
- Evaluation suite
- Quality dashboard
FREQUENTLY ASKED QUESTIONS
Straight answers.
Does RAG eliminate hallucinations?
No. RAG can reduce ungrounded answers when retrieval, instructions, and evaluation are well designed, but it still requires controls and limits.
Is a vector database mandatory?
Not in every case. The architecture can combine lexical search, semantic search, filters, existing databases, and long context as needed.
How do you respect document permissions?
Identity and authorization have to take part in retrieval, so content the user cannot access never reaches the model.
How do you measure quality?
Separately: whether the right content was retrieved, whether the answer relied on it, whether it completed the task, and whether the sources were presented correctly.
Technical sources and references
EVIDÊNCIA EM CAMPO
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Inteligência Artificial
MLRC 2025 em Princeton: cinco princípios para avaliar LLMs com resultados reproduzíveis
Cinco aprendizados da EAGLE BS no MLRC 2025, no Princeton AI Lab, sobre benchmarks, determinismo, dados, transparência e avaliação robusta de LLMs.Ler artigo ↗
Google Cloud
Google Cloud Next 2025: IA, agentes e a nova infraestrutura digital
A participação da EAGLE BS no Google Cloud Next e Partner Summit 2025: Agentspace, Gemini 2.5, agentes, cloud, dados e aprendizados para clientes.Ler artigo ↗TALK TO A SPECIALIST
Tell us the situation. We help you see the best path.
A focused conversation to understand context, risk, priority and the first workable step.