LLM Integration: -50% L1 Tickets Without Hallucinations 

LLMs connected to your real documentation. RAG architecture that cites sources, chatbots that escalate when uncertain, and zero "hallucinations" in production. EU servers, GDPR and EU AI Act compliant.

-50% L1 Tickets
90%+ RAG Accuracy
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Enterprise RAG, Chatbots, AI APIs

LLMs That Don't Make Things Up

We're not a ChatGPT wrapper. We implement RAG architecture that grounds responses in your real documentation. Multi-model (GPT-4, Claude, Llama) with no vendor lock-in. EU servers for GDPR and EU AI Act compliance.

rag/pipeline.py
# RAG Pipeline - No Hallucinations
async def query(question: str):
docs = await vector_store.search(
embed(question), top_k=5
)
if docs.confidence < 0.8:
return "No tengo información"
return llm.generate(docs, question)
90%+ Accuracy
0 Hallucinations
✓ EU Data

Service Deliverables

What you receive. Production-ready.

RAG system on your documentation
Chat interface or API endpoint
Anti-hallucination safeguards
Usage and accuracy dashboard
Feedback loop for improvement
Complete technical documentation

Executive Summary

For leadership.

LLM integration reduces customer service operational costs by 40-60% automating L1 tickets. RAG architecture ensures responses based on your real documentation, eliminating the risk of "hallucinations" that damage the brand.

Typical investment: €15,000-50,000 depending on complexity. Demonstrable ROI in 4-8 months. Full GDPR and EU AI Act compliance with EU servers.

Technical Summary

For the CTO.

RAG architecture with vector stores (Pinecone, Qdrant, ChromaDB), optimized chunks, and semantic embeddings. Multi-model support (GPT-4o, Claude, Llama 3, Mistral) with no vendor lock-in.

Security safeguards: confidence threshold, human escalation, content filtering. Deployment on your cloud (AWS, GCP, Azure) or on-premise infrastructure for maximum privacy.

Is It for You?

Production LLMs require structured data and usage volume.

Who it's for

  • Companies with high L1 ticket volume seeking cost reduction.
  • Organizations with extensive document base (FAQs, manuals, policies) underutilized.
  • CTOs needing production AI with GDPR compliance and EU data.
  • Product teams wanting AI features without building from scratch.

Who it's not for

  • Projects that only need a ChatGPT wrapper without customization.
  • Companies without structured documentation as knowledge base.
  • Budgets under €12K for a functional MVP.

Enterprise LLM Solutions

Use cases with proven ROI.

01

Customer Support Bot

Chatbot on FAQs and documentation. Resolves L1, escalates L2/L3 with context. -40-60% tickets.

02

Knowledge Assistant

Internal assistant on policies and processes. -50% onboarding time.

03

Document Processing

Structured extraction from contracts, invoices, reports. Minutes vs hours.

04

Custom API Endpoints

AI APIs integrated into your application. Classification, summary, analysis. No dependency.

Integration Process

From concept to production in 6-10 weeks.

01

Use Case & Architecture

Definition, data sources, success metrics. EU AI Act evaluation.

02

Data Pipeline

Ingestion, chunking, embeddings, vector store. 90%+ accuracy.

03

LLM Integration

Optimized prompts, safeguards, interface or API. Hallucination prevention.

04

Production & Iteration

Deployment, monitoring, continuous improvement based on real usage.

Risks and Mitigation

We anticipate problems before they occur.

Model hallucinations

Mitigación:

RAG architecture with confidence threshold and human escalation when uncertain.

Sensitive data to third parties

Mitigación:

Self-hosted models option (Llama, Mistral) with no data leaving your perimeter.

Vendor lock-in

Mitigación:

Multi-model abstraction allowing provider switches with minimal changes.

Regulatory non-compliance

Mitigación:

EU servers, GDPR documentation, EU AI Act classification included.

Why Kiwop for LLMs

We're not "ChatGPT wrapper" sellers. We've been implementing technology that generates business results for 15+ years. Responsible AI, no hallucinations, demonstrable ROI.

15+ Years of Experience
90+ RAG Accuracy
0 Hallucinations in Production

LLM Integration & RAG Chatbot Pricing

Prices updated January 2026.

Internal RAG Chatbot: €20,000-35,000
Customer System with Integrations: €40,000-75,000
Includes development, deployment, 3 months tuning
Typical ROI: 4-8 months

Executive Questions

What CTOs ask.

Does my data go to OpenAI/Anthropic?

With enterprise API, your data doesn't train models. For maximum privacy, we deploy Llama/Mistral on your cloud. Always on EU servers.

How do you prevent hallucinations?

RAG architecture that grounds responses in real documents. Safeguards that detect low confidence and escalate to human.

GPT-4, Claude, or Llama?

Depends on the case. We run comparative tests with your data. No single vendor dependency.

What happens when the LLM doesn't know?

It clearly responds that it doesn't have information. Optionally, escalates to human with conversation context.

Do you comply with GDPR and EU AI Act?

Data on EU servers. For high-risk EU AI Act: classification, technical documentation, and governance included.

Best model for sensitive data?

Llama 3 or self-hosted Mistral. Data never leaves your perimeter. Performance comparable to GPT-4.

Can I switch LLM providers?

Architecture designed for zero vendor lock-in. Abstraction that allows switching with minimal changes.

What accuracy can I expect?

90%+ accuracy with well-configured RAG. We iterate until reaching threshold before production.

Need a Chatbot or an Agent?

RAG Architecture Evaluation. We design secure integration with your data. No hallucinations, GDPR and EU AI Act compliant.

Calculate ROI
✓ No commitment ✓ Response in 24h ✓ Custom proposal
Last updated: February 2026

Technical
Initial Audit.

AI, security and performance. Diagnosis with phased proposal.

NDA available
Response <24h
Phased proposal

Your first meeting is with a Solutions Architect, not a salesperson.

Request diagnosis