Enterprise RAG: AI That Answers With Your Real Data

Your company has thousands of documents, manuals, and databases that nobody queries efficiently. RAG (Retrieval-Augmented Generation) connects that data with generative AI to deliver precise, cited, and verifiable answers.

Talk to an architect See Nexo's numbers
  • 90%+ Answer Accuracy with Your Data
  • 27 AI agents in production inside Nexo
How it works: documents, index, retrieval, model, cited answer and human approval Every correction feeds back into the system 01 · Data Your documents 02 · Index Indexed andversioned 03 · Retrieval Only what isrelevant 04 · Model Reasons withcontext 05 · Answer With citation andevidence 06 · Control A person approves
  1. 01 · Data Your documents
  2. 02 · Index Indexed and versioned
  3. 03 · Retrieval Only what is relevant
  4. 04 · Model Reasons with context
  5. 05 · Answer With citation and evidence
  6. 06 · Control A person approves
  7. Every correction feeds back into the system
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In numbers

Measurable Impact of Enterprise RAG

Market data and typical results.

  • 35.3% RAG Market CAGR Annual growth of the RAG market 2025-2035
  • -50% L1 Support Tickets Automatic resolution with RAG
  • $0.002 Cost per Query vs $5-15 with a human agent
  • 6-10 wks Implementation Time Functional RAG system in production

What's included

Service Deliverables

What you get in a complete RAG system.

  • Data ingestion: connectors for PDF, Word, Confluence, SharePoint, Notion, APIs, and databases
  • Vector database: configuration and optimization of Pinecone, Qdrant, Weaviate, or pgvector
  • Embedding optimization: model selection, chunking strategy, and metadata enrichment
  • LLM orchestration: retrieval chains, reranking, and generation with source citation
  • Evaluation and testing: accuracy metrics (faithfulness, relevance, recall) with RAGAS framework
  • User interface: chatbot or intelligent search with citations and feedback loop

Why

How a RAG System Works

The architecture that eliminates hallucinations.

RAG combines the best of both worlds: the natural language capabilities of LLMs with the precision of your actual data. When a user asks a question, the system searches for relevant information in your vector knowledge base, injects it into the LLM context, and generates a grounded response with verifiable citations. The result: answers that sound natural but are anchored in real data.

rag/pipeline.py
# Enterprise RAG Pipeline
query = "What is the returns policy?"
# 1. Embed the question
vector = embed(query) # OpenAI/Cohere
# 2. Semantic search
docs = vectordb.search(vector, top_k=5)
# 3. Rerank by relevance
ranked = reranker.rank(query, docs)
# 4. Generate with context
answer = llm.generate(query, ranked[:3])
# -> Answer + source citations ✓
  • 90%+ Accuracy
  • <2s Latency
  • Automatic Citations

Definition

What Is RAG (Retrieval-Augmented Generation)?

RAG is an AI architecture that combines information retrieval with text generation. Instead of relying solely on what the model "knows" from training, the system searches for relevant information in your data and uses it as context to generate precise, cited answers. It is the foundational architecture behind most enterprise AI strategies today.

Without RAG, LLMs generate answers based on their general training, which produces hallucinations in specific business contexts. With RAG, the hallucination rate drops from 30-40% to under 5%. The RAG market is growing at 35.3% CAGR and is projected to reach $40.3B by 2035 because it is the most reliable method for connecting AI with enterprise data without retraining models.

Summary

Executive Summary

What you need to know to decide.

Enterprise RAG turns your scattered knowledge base (documents, manuals, FAQs, databases) into an AI system that answers questions with over 90% accuracy and cites the source. The most immediate use case: customer support with a 50% reduction in L1 tickets.

Typical investment: €45,000-500,000+ (~$49,000-540,000+) depending on complexity and data volume. ROI in 4-8 months for support teams of 10+ people. The primary risk (hallucinations) is mitigated with continuous evaluation and human-in-the-loop for critical decisions.

  • -50% L1 Support Tickets
  • 4-8 months Time to ROI
  • $0.002 Cost per AI Query

For the CTO

Technical Summary for CTO

Architecture and implementation details.

Modular architecture: ingestion -> chunking -> embedding -> vectorstore -> retrieval -> reranking -> generation. Each component is interchangeable. Embeddings: OpenAI and Cohere embedding models, or open-source alternatives (BGE, E5). Vectorstores: Pinecone (managed), Qdrant (self-hosted), pgvector (native PostgreSQL).

Evaluation with the RAGAS framework: faithfulness, answer relevance, context precision, context recall. CI/CD pipeline for accuracy regression testing. Monitoring of cost per query, P95 latency, and embedding drift. Data residency stays under your control: EU servers by default for GDPR, or the cloud region you choose.

Technologies

  • Pinecone
  • Qdrant
  • Weaviate
  • pgvector
  • LangChain
  • LlamaIndex
  • OpenAI
  • Claude
  • Cohere
  • RAGAS
  • FastAPI
  • Python
  • Adaptive chunking: size and overlap optimized by document type
  • Hybrid search: combination of semantic (vector) and lexical (BM25) search
  • Reranking with cross-encoder for maximum relevance
  • Continuous evaluation with RAGAS: faithfulness >0.9, relevance >0.85

Who it is for

Is It Right for You?

Enterprise RAG makes sense when you have valuable data that nobody leverages.

Who it's for

  • Companies with extensive knowledge bases (manuals, technical docs, FAQs, regulations).
  • Support teams that answer the same questions repeatedly with scattered information.
  • Organizations that need AI grounded in proprietary data without sending sensitive information to public models.
  • Legal, compliance, or medical departments that need precise, cited answers.
  • Companies that want an intelligent internal search engine that understands natural language.

Who it's not for

  • Organizations with little documentation or low-quality unstructured data.
  • If you need creative generative AI (campaigns, content) not anchored in proprietary data.
  • Companies without budget to maintain and update the knowledge base.
  • Use cases where a traditional keyword search is sufficient.
  • If your data isn't digitized yet: you first need to digitize your knowledge.

Key points

5 Enterprise RAG Use Cases

Where RAG delivers the highest impact.

  1. 01

    Intelligent Customer Support

    A chatbot that answers customer queries by searching your knowledge base in real time. Reduces L1 tickets by 50%, responds in seconds, and escalates to a human when confidence is low. With conversation history and a feedback loop for continuous improvement.

  2. 02

    Internal Knowledge Assistant

    Employees ask in natural language and get answers from internal documentation, policies, and procedures. Reduces time spent searching for information by 40%. Especially valuable for onboarding new hires and distributed teams.

  3. 03

    Document Processing

    Extracts information from contracts, invoices, reports, and legal documents automatically. Classifies, summarizes, and answers questions about thousands of documents in seconds. Ideal for legal, compliance, and finance departments.

  4. 04

    Enterprise Semantic Search

    Replaces keyword search with semantic search that understands intent. "What is the process for returning a defective product?" instead of searching "return defect". Connects with Confluence, SharePoint, Notion, and internal systems.

  5. 05

    Sales Assistant with Product Data

    Sales teams query specs, comparisons, and pitch decks in natural language. Generates personalized proposals based on actual product data sheets and client history. 30% reduction in offer preparation time.

How we work

Implementation Process

From raw data to a production RAG system.

  1. 01

    Data Audit and Design

    We evaluate your data sources (documents, databases, APIs), define the chunking and embedding strategy, and design the RAG architecture. Deliverable: technical document with the complete pipeline.

    Week 1-2
  2. 02

    Ingestion and Vectorization

    We connect data sources, process documents, and create the vector database. Chunking optimization (size, overlap, metadata). Retrieval testing with real queries from your business.

    Week 3-5
  3. 03

    Orchestration and Evaluation

    We build the complete pipeline: retrieval -> reranking -> generation with citation. Evaluation with RAGAS (faithfulness, relevance, precision). Tuning until quality thresholds are met.

    Week 6-8
  4. 04

    Interface, Deployment, and Monitoring

    Frontend (chatbot or search), production deployment, and continuous monitoring: accuracy, latency, costs, and user feedback. 30-day post-launch support included.

    Week 9-10

Risks and how we cover them

Risks and Mitigation

Full transparency about RAG challenges.

  1. 01

    Hallucinations and incorrect answers

    Mitigation

    Continuous evaluation with RAGAS (faithfulness >0.9). Mandatory source citation. Confidence thresholds: if the system isn't sure, it says so explicitly instead of inventing an answer.

  2. 02

    Privacy and sensitive data

    Mitigation

    EU servers by default, or the cloud region you choose: on-premise, private cloud or hybrid. Granular role-based access: each user sees only what their profile permits.

  3. 03

    Scalability with millions of documents

    Mitigation

    Vectorstores designed to scale: Pinecone supports billions of vectors, Qdrant scales horizontally. Incremental indexing for new documents without reprocessing everything.

  4. 04

    Growing API and embedding costs

    Mitigation

    Per-query budgets with alerts. Semantic cache for repeated queries (-60% costs). More efficient embedding models for high volumes. On-premise open-source model option for fixed cost.

Technologies

Technology Stack

Vector databases, models, and frameworks we work with.

  • Pinecone
  • Qdrant
  • Weaviate
  • pgvector
  • LangChain
  • LlamaIndex
  • OpenAI
  • Claude
  • Gemini
  • Cohere
  • HuggingFace
  • FastAPI
  • Python
  • Docker
  • RAGAS
  • Unstructured.io

The proof

Real-World AI and Data Integration Experience

We've been integrating systems and data for 15+ years for companies in Europe and the US. Since 2023, we've deployed production RAG solutions for clients with knowledge bases spanning thousands of documents. Experiments stay in the lab: what we deliver works in the real world, with real data and GDPR compliance. Our own product, Nexo, runs on a production RAG system that retrieves over the authorized context of each company (see the case study).

  • 15+ Years in Data Integration
  • Average RAG System Accuracy 90%+
  • Average L1 Ticket Reduction 50%
  • AI Client Satisfaction 94%

Investment

Investment and timelines

We publish the ranges so neither side burns meetings on them: if the order of magnitude does not fit, better to know today than on the third call.

  • Basic RAG

    €45,000-80,000 (~$49,000-86,000)

    One data source and a chatbot that answers with citations. 6-10 weeks.

    • Ingestion of one source (PDF, Confluence, SharePoint, Notion or a database)
    • Vector store configured and chunking tuned per document type
    • Retrieval pipeline with mandatory source citation
    • RAGAS evaluation before it goes live
    • Ready-to-use chatbot, deployment and 30 days of support
    Talk to an architect
  • Mid-tier RAG Most common

    €80,000-200,000 (~$86,000-216,000)

    Several sources, semantic search and continuous evaluation.

    • Everything in the basic tier
    • Multiple data sources with automatic incremental ingestion
    • Hybrid search (semantic + BM25) and cross-encoder reranking
    • Internal semantic search engine on top of the chatbot
    • Continuous RAGAS evaluation plus cost and latency monitoring
    Talk to an architect
  • Enterprise RAG

    €200,000-500,000 (~$216,000-540,000)

    Multi-tenant, on-premise and complex integrations. 12-16 weeks.

    • Everything in the mid-tier
    • Multi-tenant with granular role-based access
    • On-premise deployment or the cloud region you choose
    • Open-weight models on your own infrastructure when data cannot leave
    • Complex integrations with core systems and a full query audit trail
    Talk to an architect

No lock-in. A closed, phased proposal after the first call.

FAQ

Frequently Asked Questions

What our clients ask about RAG.

What is RAG and why does my company need it?

RAG (Retrieval-Augmented Generation) is an architecture that connects your data with generative AI. Instead of the LLM "inventing" answers, it searches for relevant information in your documents and generates responses anchored in real data. Your company needs it if you have valuable knowledge scattered across documents that nobody queries efficiently.

How are hallucinations eliminated?

Three layers of protection: 1) Grounding: the LLM only generates answers based on retrieved documents. 2) Mandatory citation: every answer includes the source and the exact fragment. 3) Confidence thresholds: if relevance is low, the system responds "I don't have enough information" instead of making something up.

How much does it cost to implement a RAG system?

Basic project (1 data source, simple chatbot): €45,000-80,000 (~$49,000-86,000). Mid-tier project (multiple sources, semantic search, evaluation): €80,000-200,000 (~$86,000-216,000). Enterprise project (multi-tenant, on-premise, complex integrations): €200,000-500,000+ (~$216,000-540,000+). Always with a detailed proposal and estimated ROI.

How long does implementation take?

A functional RAG system in production: 6-10 weeks. Includes data audit, ingestion, vectorization, retrieval pipeline, evaluation, interface, and deployment. Enterprise projects with multiple integrations: 12-16 weeks. A functional prototype is available by week 4.

Is it secure? Is my data protected?

Yes. EU servers by default, with full GDPR compliance, or the cloud region you choose if you need a different data residency (private cloud, on-premise or hybrid). Your data is never used to train third-party models. Granular role-based access and complete query audit trail.

What document formats are supported?

Virtually all of them: PDF, Word, Excel, PowerPoint, HTML, Markdown, Confluence, SharePoint, Notion, Google Docs, SQL databases, REST APIs, and plain text. We use Unstructured.io for advanced processing of complex documents with tables, images, and irregular layouts.

Does it update automatically when data changes?

Yes. We configure incremental ingestion: when a document is added or modified, it's reprocessed and updated in the vector database automatically. Options: webhooks (real-time), cron jobs (periodic), or manual trigger. No need to re-index the entire database. Keeping that index fresh, monitored and evaluated in production is part of LLMOps.

Can I use open-source models instead of OpenAI?

Absolutely. Our architecture is model-agnostic. You can use open-weight models (Llama, Mistral, Qwen), or any HuggingFace model deployed on your own infrastructure. This eliminates vendor dependency and reduces per-query cost to virtually zero (infrastructure only). Ideal for highly sensitive data that cannot leave your network.

Next step

Which documents does nobody actually read?

30 minutes with the person who will design the system, not with a salesperson. We tell you what can be built with your data, what it would cost and what is not worth doing. We reply in under 24 hours. If you would rather start with the full diagnosis: AI audit from €3,000.

Talk to an architect
  • No commitment
  • Response in 24h
  • Custom proposal

Case studies

Projects where we put it to work

Nexo
AI & SaaS · Our Own Product

Nexo

10+ Live integrations
90% Automated time entries
View case
Last updated: July 2026

Let's talk.

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AI, security and performance. Diagnosis with phased proposal.

  • NDA available
  • Response <24h
  • Phased proposal

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