LLMs, RAG, AI agents and automation integrated into your software and processes. No "AI for the hype" — only where it measurably reduces time, errors or cost. Functional POC in 4 weeks, EU AI Act and GDPR compliance by design.
Three patterns we see most often in European businesses — SMB to enterprise — coming to us asking "could we use AI here?". The answer is almost always yes — but it has to be designed right.
Contracts, tickets, documentation, customer conversations, manuals… valuable information buried in PDFs, drives and databases. Nobody has time to extract it manually.
Classifying emails, copying data between systems, generating reports, answering the same questions, reviewing documents one by one. Manual work that scales with cost.
70% of tickets repeat the same questions, but answering each one takes time. SaaS chatbot tools give generic responses that frustrate customers.
Eight capabilities covering 95% of real use cases a business in Valencia, Madrid, Barcelona, London or anywhere in Europe asks for. We start with a functional POC in 4 weeks — if it validates, we scale to production.
We integrate GPT-4, Claude, Gemini, Llama and Mistral into your software. When justified, fine-tuning on domain data for precision and brand voice.
Retrieval-Augmented Generation over documentation, contracts, manuals or databases. AI answers with your real information, not generic responses — and cites the source.
Agents that execute real workflows with tool use, function calling and MCP (Model Context Protocol). Multiple steps, intermediate decisions, integration with your systems.
Conversational assistants for internal or external support. Multilingual (EN, ES, IT…), integrated with your CRM/ERP, with human handover and satisfaction metrics.
Mass document processing: field extraction in invoices, contract analysis, email classification, PDF anonymization and automatic executive summary generation.
Computer vision for manufacturing QA, retail analytics (footfall, planogram), biometrics, and medical or industrial image processing.
Applied ML on real problems: churn prediction, demand forecasting, lead scoring, fraud detection and industrial predictive maintenance.
Not sure where to start? We audit your use cases, prioritize them by expected ROI and deliver a functional POC in 4 weeks — to validate with real data before investing in production.
The EU AI Act is fully applicable from August 2026. Fines reach up to €35M or 7% of global turnover. Every AI system we build is compliant by default.
Risk classification, mandatory technical documentation, human oversight on critical decisions, bias monitoring, end-user transparency, logging and audit. Cross-functional with our GDPR and NIS2 services.
We don't start building until we know it makes sense. Workshop, POC with real data, informed decision. Then we scale to production with real MLOps.
1–2 week workshop to map use cases, prioritize by expected ROI and discard those that don't make sense. No commitment to continue.
Functional prototype on real data with the prioritized use case. Clear success metrics agreed beforehand. If it doesn't work, you know in a month.
If the POC validates, we scale to production with compliance, MLOps, observability, continuous eval and CI/CD for models. Fixed cost per milestone.
Drift monitoring, A/B testing between models, scheduled retraining and prompt improvement with real usage data. AI isn't delivered, it's operated.
We're not married to any vendor. We choose model, framework and stack based on the use case, latency, cost and privacy requirements. Open architecture, no vendor lock-in.
The questions we always hear before starting an AI project. If yours isn't here, drop us a line — we reply in under 24h.
RAG (Retrieval-Augmented Generation) lets an LLM answer with your company's real information — documents, contracts, databases, knowledge bases — instead of generic responses. It reduces hallucinations, guarantees source citations, and lets the AI know your business without retraining. It's the standard architecture for enterprise chatbots and internal assistants in 2026.
Yes, with the right architecture. We use enterprise endpoints (Azure OpenAI, Anthropic Enterprise, Google Vertex AI) with GDPR compliance, EU data residency, no use for training, and encryption in transit and at rest. For especially sensitive data we deploy open-source models (Llama, Mistral) on-premise or in private cloud. NIS2 and AI Act compliance by design.
In 90% of cases, no. Inference with commercial LLMs (OpenAI, Anthropic, Google) runs in the cloud and is pay-as-you-go. For high-volume scenarios or strict privacy requirements we deploy open-source models on cloud GPU (AWS, OVHcloud, Azure) or on-premise. We advise on the make-or-buy decision based on your volume, latency and compliance.
We combine several techniques: RAG with mandatory source citations, output validation against structured schemas (JSON Schema, Zod), programmatic guardrails, prompt engineering with few-shot examples, domain fine-tuning when applicable, and continuous evaluation with proprietary datasets. Every critical output passes through validation layers before reaching the user.
Yes. We design every AI system with EU AI Act by default: risk classification, mandatory technical documentation, logging and observability, human oversight on critical decisions, bias monitoring and end-user transparency. Full GDPR compliance: lawful basis, data minimization, data subject rights, and DPIA when required. Cross-functional with our NIS2 and GDPR services.
It works with what you already have. We integrate AI on top of your current systems — Salesforce, HubSpot, SAP, Odoo, Holded, Notion, Confluence, SharePoint, SQL/NoSQL databases — via APIs, native connectors and MCP (Model Context Protocol). No migration, no replacement. AI should enhance your stack, not force you to change it.
AI works best when integrated with custom software, cybersecurity and team training. Check out the other pillars.