LLM Integration for Business: On Your Data, in Production
We connect an LLM to your documentation and put it in production in 6-10 weeks. It answers citing the source and, if it cannot find one, hands over to a person.
- 90%+ RAG Accuracy
- 01 · Systems Reads ERP, CRM and email
- 02 · Goal Plans the steps
- 03 · Proposal With the evidence
- 04 · Control A person approves
- 05 · Action Acts in your systems
- 06 · Log Everything is traced
- Learns from every approval
- ISO 27001 Information security
- ISO 42001 AI management system
- ENS Spanish National Security Framework
- 5.0 on Clutch · verified reviews
- Since 2009 Software and AI engineering
In numbers
Production Results
Real financial impact of well-implemented LLMs.
- -50% L1 Tickets Deflected by chatbot
- €0.02 Cost/Query Including infrastructure, vs €8-15 for a human agent
- 24/7 Availability No shifts or holidays
Why
Enterprise RAG, Chatbots, AI APIs
LLMs That Answer From Your Documentation and Cite the Source
We're not a ChatGPT wrapper. We implement RAG architecture that grounds responses in your real documentation. Multi-model (frontier and open-source models) with no vendor lock-in. EU servers for GDPR and EU AI Act compliance.
- 90%+ Accuracy
- ✓ Cites source
- ✓ EU Data
Definition
What is RAG and why does it reduce hallucinations?
RAG (Retrieval-Augmented Generation) searches your documentation before answering. The LLM answers from what it finds and cites the source. If it finds nothing relevant, it says so and hands over to a person. The difference between a chatbot that damages your brand and one that empowers it.
What's included
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
Summary
Executive Summary
For leadership.
LLM integration reduces customer service operational costs by 40-60% automating L1 tickets. With RAG, the system answers citing your documentation and, if it cannot find the answer, hands over to a person. That reduces the "hallucinations" that damage the brand.
Demonstrable ROI in 4-8 months. Scalable solution adapted to each case's complexity. Designed to comply with GDPR and the EU AI Act, on EU servers.
For the CTO
Technical Summary
For the CTO.
RAG architecture with vector stores (Pinecone, Qdrant, ChromaDB), optimized chunks, and semantic embeddings. Multi-model support (frontier and open-source models) 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.
Who it is for
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.
- Projects without enough data volume or tickets to justify a functional MVP.
Key points
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. If your application runs on Laravel, see Laravel AI integration.
How we work
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 control.
- 04
Production & Iteration
Deployment, monitoring, continuous improvement based on real usage.
Risks and how we cover them
Risks and Mitigation
We anticipate problems before they occur.
- 01
Model hallucinations
MitigationRAG architecture with confidence threshold and human escalation when uncertain.
- 02
Sensitive data to third parties
MitigationSelf-hosted models option (Llama, Mistral) with no data leaving your perimeter.
- 03
Vendor lock-in
MitigationMulti-model abstraction allowing provider switches with minimal changes.
- 04
Regulatory non-compliance
MitigationEU servers, GDPR documentation, EU AI Act classification included.
Technologies
LLM Technologies
Multi-model, no dependency.
- OpenAI GPT
- Anthropic Claude
- Llama
- Mistral
- LangChain
- LlamaIndex
- Pinecone
- Qdrant
- ChromaDB
- Python
- FastAPI
The proof
Why Kiwop for LLMs
We're not "ChatGPT wrapper" sellers. We've been implementing technology that generates business results since 2009. And we use it ourselves: the kiwop.com chatbot runs on Claude via the Anthropic API, and our own platform Nexo integrates LLMs in production every day.
- 17 Years of Experience
- 90+ RAG Accuracy
- 6-10 Weeks to Production
What's included
What Each LLM Project Includes
Tailored solutions with demonstrable ROI.
- Internal RAG Chatbot on your documentation
- Customer System with CRM integrations and escalation
- Includes development, deployment, 3 months tuning
- Typical ROI: 4-8 months
Sources
Sources for the Figures on This Page
Where the privacy, regulatory and case data we cite comes from. The other figures are estimates from our own experience. Accessed 26 September 2026.
- OpenAI, Enterprise privacy By default, OpenAI does not train its models on data sent through the API or its business plans.
- Anthropic Privacy Center: Is my data used for model training? By default, Anthropic does not use inputs and outputs from its commercial products, including the API, for training.
- Case study: Nexo Kiwop’s own platform, running LLMs and AI agents in production every day.
- Regulation (EU) 2016/679 (GDPR), EUR-Lex Official data protection text.
- Regulation (EU) 2024/1689 on artificial intelligence (EU AI Act), EUR-Lex Official text of the risk classification and obligations we mention.
FAQ
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 reduce hallucinations?
RAG architecture that grounds responses in real documents. Safeguards that detect low confidence and escalate to a human. That cuts hallucinations sharply, but not to zero, which is why every answer cites its source.
Commercial API or open-source model?
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?
Open-source models like Llama or Mistral, self-hosted. Data never leaves your perimeter. Performance comparable to frontier models.
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. After launch, LLMOps keeps that accuracy stable.
LLM Integration or AI Agent?
LLM integration covers query and response: chatbots, enterprise RAG and APIs. An AI agent also executes actions in your systems. If unsure, AI consulting clarifies the approach before you invest.
Next step
What do you want to connect to an LLM?
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.
- No commitment
- Response in 24h
- Custom proposal
Case studies
Projects where we put it to work
Loviux
La Salve
Nexo
Let's talk.
Initial technical consultation
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.
Talk to an architect