Artificial Intelligenceapplied toreal industry.
We build AI applied to industry: custom MCP servers that give LLMs scoped access to third-party industrial systems, AI-native products that solve specific process problems, disciplined prompt engineering for technical assistants and code analyzers. Computer vision and predictive maintenance when the problem requires it.
Three principles on how we apply AI.
AI solves real industrial problems, it doesn't fill slides. Integrated with existing systems, designed with rigour, validated on the client's process KPI.
MCP-first: AI that acts
We build custom MCP (Model Context Protocol) servers giving LLMs scoped, controlled access to ERP, PLM, SCADA, industrial databases, PLC code. AI doesn't just read PDFs — it queries the real systems and, where it makes sense, runs constrained actions.
Disciplined prompt engineering
Access to an LLM is not enough. We design prompts, retrieval, guardrails and tool calling per use case, testing them on real client scenarios. We have specific in-house expertise — prompt engineering is a craft.
AI as a tool, not as the point
Every AI application solves a concrete problem (understanding a fault, predicting a stop, tracing a part, supporting a maintenance engineer). Our solutions don't force AI in at any cost — we integrate it when it's genuinely needed. Value is measured in time saved and errors avoided.
PaLoMa AI
Build AI agents on your systems. In days, not months.
Standalone platform for chatbots and AI agents over your data and processes. Multi-LLM, multimodal RAG, custom MCP on demand, enterprise governance, no-code admin — out-of-the-box, no vendor lock-in.
- Custom MCP on demand over your systems
- Multimodal RAG and enterprise governance built-in
- Already in production at an automotive company
The technologies behind our AI solutions.
Pragmatic, vendor-neutral where it makes sense. We pick the model and architecture that fit the use case — there is no universal answer.
Foundation models
The engines. Picked by use case, cost, data constraints.
AI ↔ systems integration
Custom MCP servers, function calling, agents that operate on client systems.
Retrieval & vector DB
Company-knowledge memory: technical docs, manuals, regulations.
Computer Vision & classical ML
When the problem does not need an LLM: detection, classification, time series.
MLOps & inference
Versioned models, reproducible deploys, on-edge or cloud inference.
Product traceability via computer vision
Industrial vision system that recognises product codes — printed or labelled — on moving parts in real time across production lines, buffers, and warehouse entries/exits. Every transition is logged automatically. FITEC internal POC: Tracking Code.
Line-failure prediction
Machine learning models trained on sensor streams (vibration, temperature, current, pressure) of a production line to predict imminent line blockages and compromised parts before they occur. Streaming anomaly detection + classification of the failure type, with useful lead time for the maintenance team.
AI assistant for machine stoppages
During a line stoppage the assistant analyses in real time the plant's PLC code, cross-referencing it with the line's technical documentation. The maintenance engineer asks in natural language 'why did it stop?' and the AI replies citing the specific PLC code lines involved, the critical I/O and the documented procedures. RAG over docs.
From problem to AI in production.
Five stages, short iterations. We validate against process value at every step — we don't build a model unless we're confident it will move the right KPI.
Frequently asked.
— 01 What is MCP and why do you use it?
— 02 Are you tied to a specific LLM?
— 03 Is prompt engineering really a craft?
Got an industrial process to digitize?
Tell us the problem. We'll design the right solution together, from proof-of-concept to production.