AI CONNECTED TO YOUR KNOWLEDGE
Enterprise RAG consulting: AI that answers from your own documents, with 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 RAG (retrieval-augmented generation)?
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
What RAG is and why companies use it
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.
IN DEPTH · 1/4
How RAG works, step by step
The basic flow
RAG takes a question, looks for the most relevant passages in authorized sources, hands those passages to the model as context and generates an answer that says where the information came from.
RAG or fine-tuning
RAG suits knowledge that changes and needs sources: policies, contracts, manuals. Fine-tuning adjusts a model's behavior or style and does not replace consulting up-to-date documents.
What RAG does not solve
Outdated, contradictory or disorganized documents produce poor answers. And RAG reduces, but does not eliminate, unsupported answers.
IN DEPTH · 2/4
Architecture of an enterprise RAG system
Answer quality is decided, to a large extent, before the model comes into play.
Ingestion and cleaning
Documents from several sources and formats are extracted, cleaned and enriched with information such as origin, date and owner.
Chunking
Text is split into passages that preserve meaning and context. The chunking strategy changes the result a lot.
Search index
We combine semantic search with embeddings and keyword search, with filters, depending on the type of question and document.
Reranking
A second pass reorders the retrieved passages by relevance before sending them to the model, which usually improves precision.
Answer with sources
The model answers from the retrieved context, cites its sources and states when it did not find enough information.
Permissions in retrieval
Identity and authorization take part in the search, to keep content a person cannot access from being sent to the model.
IN DEPTH · 3/4
How we measure the quality of a RAG system
Without measurement, RAG becomes a convincing but unreliable demo. We evaluate each stage separately.
Retrieval
Was the right content found among the top results? We measure it with test questions whose correct answers are already known.
Grounding
Is the answer based on what was retrieved, without inventing information that is not in the sources?
Usefulness
Does the answer solve the asker's task, clearly and in the expected format?
Cost and latency
How much each answer costs and how long it takes. A system has to be viable to operate, not just correct.
Continuous monitoring
Sources change and new questions appear. We track quality over time and adjust indexes and rules.
IN DEPTH · 4/4
Where RAG creates the most value
Support and service
Consistent answers based on manuals, policies and case history, for teams and for customers.
Legal and compliance
Querying contracts, standards and policies with the source passage indicated, to speed up human review.
Engineering and technical documentation
Questions about systems, procedures and specifications spread across many sources.
People and internal policies
Recurring questions about benefits, rules and processes, with up-to-date answers.
Sales and proposals
Reuse of approved proposals, cases and materials to answer customers consistently.
OUTCOMES
What a good RAG system delivers
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
When it makes sense to implement RAG
- 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
How we implement RAG, from data source to evaluation
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
What you receive in a RAG project
- RAG architecture
- Ingestion pipeline
- Index and search
- Answer API
- Evaluation suite
- Quality dashboard
TRACK RECORD
Who builds your RAG
Software engineering, data and Google Cloud in the same team, with more than 10 years in business and more than 170 projects delivered.
- 10+
- years in business and Google partnership
- 170+
- projects delivered
- 97%
- NPS
FREQUENTLY ASKED QUESTIONS
Frequently asked questions about enterprise RAG
What is RAG (retrieval-augmented generation)?
It is a technique that combines search over information sources with a language model. Instead of answering only from what it learned in training, the model receives relevant passages from your documents and answers based on them, citing the sources.
What is the difference between RAG and fine-tuning?
RAG brings current, traceable knowledge into the answer by consulting documents at question time. Fine-tuning adjusts the model's behavior, such as style and format, and is not the best way to keep knowledge that changes.
Does RAG work with PDFs, spreadsheets and internal systems?
Yes. Sources can include PDFs, documents, spreadsheets, wikis, email and internal systems reached through APIs. Each format needs its own handling at ingestion, and the original permissions must be respected.
How long does it take to implement a RAG system?
It depends on document quality, permissions, integrations and the quality bar required. That is why we start with a prototype on a few sources and work in cycles with evaluation, and the timeline is set from those results.
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
FIELD EVIDENCE
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Artificial Intelligence
MLRC 2025 at Princeton: five principles for reproducible LLM evaluation
Five EAGLE BS lessons from MLRC 2025 at Princeton AI Lab on benchmarks, determinism, data, transparency, and robust LLM evaluation.Read article ↗
Google Cloud
Google Cloud Next 2025: AI, agents, and the new digital infrastructure
EAGLE BS at Google Cloud Next and Partner Summit 2025: Agentspace, Gemini 2.5, agents, cloud, data, and practical lessons for customers.Read article ↗TALK TO A SPECIALIST
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