Skip to main content

AI integration for business

Take your business to the next level with artificial intelligence. From conversational chatbots to predictive analytics and intelligent automation.

What does integrating AI into a business mean?

Integrating AI into a business means connecting language models and machine learning models to the data and tools the company already uses, so they can handle specific tasks: chatbots and virtual assistants, predictive analytics, natural language processing, computer vision, or RAG systems that answer using your own documentation. At Leonwebs, an agency based in León (Spain), we validate each use case with an MVP before scaling it.

Benefits of integrating AI into your business

Intelligent automation

Reduce repetitive tasks with AI that learns and improves over time.

Data-driven decisions

Predictive analytics and ML models that turn data into competitive advantages.

Superior user experience

Chatbots, personalized recommendations, and smart search that build customer loyalty.

Scalability

We design each solution so it can grow with your business's request volume.

What AI integration includes

  • Chatbots and virtual assistants with conversational AI
  • Predictive analytics and machine learning models
  • Natural language processing (NLP)
  • Computer vision and image recognition
  • Integration with GPT, Claude, Gemini, and AI models
  • RAG (Retrieval-Augmented Generation) with your own data

Artificial intelligence technologies

  • Python
  • TensorFlow
  • PyTorch
  • OpenAI API
  • Anthropic API (Claude)
  • LangChain
  • Hugging Face
  • vectorDBs

How we integrate AI

  1. Data and process audit

    We identify AI opportunities in your business and available data.

  2. Prototype and validation

    Functional MVP to validate impact before scaling.

  3. Development and integration

    Integration with your existing systems and model training.

  4. Monitoring and improvement

    Performance metrics and continuous model retraining.

Frequently asked questions about AI integration

What is RAG and what is it used for?

RAG (Retrieval-Augmented Generation) is a technique that, before answering, searches your own documents and data for the relevant information and passes it to the language model. This way the assistant answers with your company's content, such as manuals, catalogs, or knowledge bases, and not only with what it learned during training, with no need to retrain the model. It usually relies on a vector database.

Which AI models do you work with?

We work with OpenAI's GPT models, Anthropic's Claude, and Google's Gemini, as well as open models through Hugging Face. We integrate them through their APIs and with LangChain, and for custom models we use Python with TensorFlow and PyTorch. We choose the model based on the use case, the cost, and the data privacy requirements.

Where does an AI project start?

With a data and process audit: we identify the tasks where AI adds value and the data you have available. Then we build a functional MVP to validate the impact before scaling, integrate it with your systems and, once it is in production, track its metrics to keep improving it.

Other Leonwebs services

Ready to get started?

Tell us about your project and we'll help you make it happen.

Request a quote