Applied intelligence

Enterprise AI Services

We design, build and operate AI systems in production that process thousands of documents, automate decisions and scale with human oversight, with business metrics from day one of operation.

  • 90%+ RAG accuracy in real projects
  • 30–70% Typical process time reduction

We implement AI where it delivers measurable value: process automation, knowledge extraction from documents, and assistants that understand your business. Before writing code, we assess your data quality, define success metrics and establish governance. Every project includes hallucination monitoring and inference cost control.

Our own platform, Nexo, runs Kiwop's day-to-day operations with 27 AI agents. And we document it with data: Kiwop Labs publishes our experiments, measurements and reproducible tests.

Definition

What is enterprise artificial intelligence?

Enterprise AI is the application of machine learning algorithms and language models (LLMs) to automate processes, extract knowledge from unstructured data, and make decisions at scale. Unlike academic AI, enterprise AI prioritizes measurable ROI, integration with existing systems, and regulatory compliance (EU AI Act). At Kiwop we implement three types of solutions: RAG systems (Retrieval-Augmented Generation) for queries on internal documentation, autonomous agents for complex workflows, and intelligent automation with n8n, Make, and Python to eliminate repetitive tasks.

Applied intelligence

Our capabilities

AI solutions with measurable results in production

AI Consulting

Strategy

AI adoption strategy. Governance, EU AI Act compliance, implementation roadmaps.

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LLM Integration

RAG & Agents

Custom GPTs, RAG systems and AI APIs. LLMs that understand your business on your documentation.

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Process Automation

Smart RPA

Eliminate repetitive tasks with n8n, Make and Python. Smart workflows that connect your systems.

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Agentic AI Development

Agents

Autonomous LLM-based agents for complex workflows and decision-making.

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Enterprise RAG

Knowledge Base

Production RAG systems for queries on internal documentation with over 90% accuracy.

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AI Chatbots

Conversational

Intelligent conversational assistants that understand your business and serve 24/7.

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AI Personalization

Personalization

AI-powered personalized experiences: recommendations, dynamic content and predictive segmentation.

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LLMOps

Operations

Language model operations: monitoring, evaluation, deployment and cost control.

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GEO: AI Search Optimization

ChatGPT & AI

Get your brand cited by ChatGPT, Claude and Perplexity. Optimization for generative engines.

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Intelligent Search

Elasticsearch

Search with Algolia and Elasticsearch: +35% conversion and -60% zero-result searches.

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EU AI Act Compliance

EU AI Act

EU AI Act compliance audit, risk assessment and regulatory documentation.

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Nexo SGSI

ISO 27001 · ENS · ISO 42001

ISO 27001, ENS and ISO 42001 certification with the platform we used to pass our own audit with zero non-conformities.

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Why

From proof of concept to production

Many AI projects stall at the proof of concept: they work with ten documents and fail with ten thousand, the model invents critical data, or inference costs spiral.

We deploy to production: RAG systems with audited accuracy, robust data pipelines and hallucination monitoring. A system is finished when it processes real data in production.

  • 27 AI agents in production in Nexo, our own platform
  • 0 non-conformities in the ISO 27001, ISO 42001 and ENS audit
Artificial intelligence in production

Who it is for

Who it's for

  • Companies with proprietary data (documents, historical records, knowledge bases)
  • Operations with repetitive tasks consuming +20h/week
  • Teams that already tried ChatGPT and need something in real production
  • Organizations with compliance requirements (EU AI Act, GDPR)

Who it's not for

  • If there is no structured data or internal documentation
  • Exploratory projects without a defined use case
  • If there is no plan to invest in data quality
  • Startups without established processes to automate

How we work

Our methodology

From data audit to production in 12 weeks

  1. 01

    Data audit

    Evaluation of quality and volume of your data. We identify which use cases are viable with your current information and which require enrichment.

  2. 02

    Scoped proof of concept

    PoC with real data and defined success metrics. Maximum 4 weeks to validate technical viability before investing in production.

  3. 03

    Development for production

    Scalable architecture with hallucination monitoring, inference logging and cost control. Testing with identified edge cases.

  4. 04

    Deployment and governance

    Production system with performance metrics, configured alerts and EU AI Act compliance documentation if applicable.

FAQ

Frequently asked questions about enterprise AI

We answer the most common questions about AI implementation

What types of AI projects does Kiwop implement?

We implement three main categories: RAG systems (Retrieval-Augmented Generation) for queries on internal documentation with over 90% accuracy, autonomous LLM-based agents for complex workflows, and intelligent automation with n8n, Make, and Python. All our projects are deployed in production with hallucination monitoring and cost control.

How long does it take for an AI project to deliver measurable results?

Our process includes a scoped proof of concept of no more than four weeks to validate technical feasibility. Complete projects are deployed to production within 8-12 weeks. Business metrics are tracked from day one of operation in real-time dashboards.

What differentiates Kiwop from other AI consultancies?

We deliver systems in production that process real data, with hallucination monitoring, inference cost control and EU AI Act compliance documentation. We operate our own platform, Nexo, with 27 AI agents, and we publish our measurements in Kiwop Labs.

What accuracy do the RAG systems you develop achieve?

Our RAG systems achieve over 90% accuracy in real projects, measured with systematic evaluations on production queries. We implement advanced chunking techniques, hybrid reranking, and optimized prompts to minimize hallucinations.

Do you comply with the EU AI Act in your projects?

Yes. All our projects include risk assessment according to the EU AI Act, training data traceability documentation, and governance protocols. For high-risk systems, we implement bias audits and explainability according to European regulation requirements.

Applied intelligence

Let's talk.

AI feasibility audit

We assess your data and tell you what can be built with it. AI, security and performance in production.

  • RAG and agents
  • AI governance
  • Response within 48 hours

We assess the quality of your data and present the viable use cases with an estimated return.

Request the audit