Machine Learning & Deep Learning
We develop sophisticated ML/DL models tailored to your specific business needs, from predictive analytics to computer vision and natural language processing.
Artificial Intelligence
Machine Learning, Generative AI & Agentic AI Solutions
Concrete AI applications
Our AI solutions use the most mature tools available today to help businesses automate processes, gain insights from data, and create intelligent systems that learn and adapt.
Our approach to agentic AI always keeps humans in the loop: agents read, prepare and propose; decisions and sign-off stay with people.
Large Language Models & Generative AI
We integrate and customize the main commercial LLMs (ChatGPT, Claude, Gemini) and open-weight models like Llama, Gemma, DeepSeek and Qwen. We develop generative AI solutions for text, images, and code.
Retrieval-Augmented Generation (RAG) & Local LLMs
We implement RAG with local or cloud LLMs to integrate your proprietary data. We offer private solutions with open-weight models like Llama, Gemma, DeepSeek and Qwen, keeping data and control on-premise.
Agentic AI & Multi-Agent Systems
We design AI agents with reasoning, persistent memory and tool-use capabilities: they read, prepare and propose, while decisions stay with people. We build multi-agent systems with the leading agentic frameworks, implementing A2A architectures for interoperable agents.
Model Context Protocol (MCP)
We implement MCP to enhance AI capabilities by providing contextual awareness and access to external tools and data sources, making AI systems more powerful and versatile.
AI Ethics & Governance
We ensure responsible AI development with robust governance frameworks, bias detection, transparency measures and human approval of critical actions to build trustworthy AI systems.
AI Integration
We integrate AI capabilities into your existing systems and workflows, getting the most out of the technology investments you've already made.
Custom AI Development
We build bespoke AI solutions tailored to your specific industry challenges, from healthcare diagnostics to financial forecasting and beyond.
AI for Sensitive & Healthcare Data
We implement secure AI solutions for sensitive and healthcare data, with local models, advanced encryption, and regulatory compliance to ensure privacy and security.
Our AI Methodologies
At noze, we employ a comprehensive approach to AI development, combining proven methodologies with innovative techniques to deliver solutions that drive real business value.
Our AI expertise includes:
- Supervised, unsupervised, and reinforcement learning
- Neural networks and deep learning architectures
- Natural Language Processing (NLP) and Understanding (NLU)
- Computer Vision and image recognition
- Time series analysis and forecasting
- Anomaly detection and pattern recognition
- LLM Implementation & Fine-tuning (ChatGPT, Claude, Gemini, Llama, Mistral, Gemma, DeepSeek, Qwen)
- Agentic AI Frameworks: LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, smolagents, Google ADK, Cheshire Cat
We work closely with your team to understand your specific challenges and objectives, then develop AI solutions that deliver measurable results. Our development process is iterative and includes continuous improvement and adaptation to changing business needs.
Frequently Asked Questions
What types of AI projects have you delivered?
We have delivered a wide range of AI projects: predictive analytics systems, custom enterprise chatbots with LLMs, computer vision systems for quality control, NLP solutions for document analysis, human-in-the-loop AI agents and multi-agent pipelines, and RAG systems with local and cloud models.
How do you integrate AI into existing systems?
Our approach to AI integration is modular and flexible. We start with a thorough analysis of existing systems, identify optimal integration points, and develop AI solutions that fit into existing workflows, minimizing operational disruption.
What AI technologies and frameworks do you use?
We work with a mix of well-established industry technologies: TensorFlow, PyTorch and Keras for deep learning; leading commercial and open-weight models for generative AI; the main agentic frameworks for multi-agent systems; and MCP (Model Context Protocol) for integration with external tools and data. The choice depends on specific project requirements.
How do you handle data privacy in AI projects?
Data privacy is our top priority. We implement rigorous security measures, use data anonymization techniques, and follow industry best practices and privacy regulations like GDPR. We also offer on-premise AI solutions for particularly sensitive data.
How long does it take to implement an AI solution?
Implementation times vary based on project complexity. Typically, a proof-of-concept can be developed in 4-8 weeks, while a complete solution may take 3-6 months. We adopt an agile approach with incremental releases to deliver value from the early stages of the project.
Do your AI agents act autonomously?
No: in our systems, agents read, prepare and propose, but any action with real impact (sending, signing, making changes) always requires a person's approval.
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