Prometeo

Prometeo

We turn chaos into clarity

Start-up of ITA - Italian Trade Agency

ITA - Italian Trade Agency

Start-up

www.prometeonet.it
info@prometeonet.it

Viale Pietro Toselli,  94
Siena  (Siena) — 53100 — Italia
Phone 3486021690

Description

Prometeo works on improving decision quality in complex systems, where each choice affects multiple variables and outcomes are not always immediately visible.

In many real-world contexts, available data is fragmented and difficult to interpret in a consistent and reliable way.

Prometeo addresses this challenge by translating this complexity into mathematical models that support more solid and verifiable decisions. In these contexts, the goal is not to simplify the problem, but to build a structure that makes complexity understandable and governable.

This approach is applied in companies operating in complex environments, where decisions depend on large volumes of heterogeneous data and require reliability, consistency, and integration into operational processes.

The result is a clearer and more reliable decision process, based on explicit and measurable criteria, designed to support scalable and reliable decisions across different application domains. Technologies such as artificial intelligence support this approach as tools within a method built on rigor, measurability, and accountability.

Our mission is to bring scientific rigor into decision-making by transforming complex and fragmented data into structured, measurable, and comparable information.


Our products

Visual Analysis - From visual data to decision insight

Visual Analysis - From visual data to decision insight

Our solution enables the transformation of visual information into data that can be used within decision-making processes. In many operational contexts, a significant part of the available information comes from direct observations, yet it remains difficult to compare, track, and use effectively. This approach makes these observations comparable by building indicators that allow their evolution to be interpreted over time and at scale, preserving detail while making it usable within decision processes. It is applied across different domains, including agriculture and quality control. In viticulture, it supports the assessment of grape maturation and early disease detection, while in quality control it enables systematic identification of defects and anomalies. A key advantage is its efficiency in training: it does not require large datasets or long training times, while maintaining high reliability. It performs effectively even in noisy and unstructured environments. The result is a scalable and easy-to-deploy solution that supports more consistent and data-driven decisions.
Predictive Maintenance - Early detection of failures and anomalies

Predictive Maintenance - Early detection of failures and anomalies

In industrial environments, failures rarely occur suddenly; they are usually preceded by signals distributed across sensors, logs, and maintenance history. The challenge is not the availability of data, but how it is interpreted over time. Our approach analyzes data from machines and industrial assets to identify patterns in their behavior and detect early signals that precede failures. It is designed to operate efficiently without requiring large volumes of data, while remaining robust in complex and noisy environments. It allows the detection of anomalies and weak signals that are often overlooked by traditional methods. The result is a scalable, cost-effective, and easy-to-integrate solution that enables earlier interventions and improves operational continuity.