Elecnor María Elena Kimal project consists of an App for the reporting of the progress of multiply activities in site, with or without internet connection.
Reports are given by a supervisor of a work team, who can configure them for individual or group activities. The report also include a percent of progress, details of machinery, used equipment, vehicles, workers, observations, date/time of start and end activities, and images that can be uploaded from the phone or taken from its camera.
Elecnor app allows project administrators to manage staff and data necessary for the activities.
Firecatch, a smartphone application fo the reporting of nearby forest fires. When the report give by the users is validated, an alert is immediately raised, notifying the closest persons of the event, accelerating the control and combat processes of the fire.
Reports submitted by users include incident location, images, comments and user data.
We also developed a backoffice web platform, where fire inspectors can approve and eliminate reports made bu users and access information on past events such alert history, images and geolocation.
Platform for the prediction and behavior of forest fires, wich uses AI and Machine Learning algorithms, created specifically for Acot company.
A simulation is displayed on an interactive map, wich shows the spread of the fire over time, based on topographical and climatic variables and the fuel model that describes it the best. These variables can be modified to evaluate different scenarios and combat strategies.
Platform created for Antu company, which allows the management and monitoring of electric vehicles and charges in real time.
Movia platform delivers intelligent information, providing indicators that allows the creation of strategies for users that indicates then and how to charge their vehicles, reducing energy costs.
Tetris platform developed and designed by Forcast, uses artificial intelligence for the improvement of efficiency in logistics porcesses, wich optimize significantly the space in a supplier’s warehouse, the loading and distribution of space inside trucks and the capacity of the center of distribution.
The platform also organize the boxes according to their delivery priority and designs an specific route according tot he order of their load, all this trough an interactive 3D interface, which makes it simple and easy to use.
Looking for a way to predict energy production in order to analyze the market and thus economically value of a solar power plant, we developed a monitoring platform to optimize the performance of solar plants, which standardize the data capture and detect failures in the panels analyzing the information obtained.
This brought as a main benefit a significant decrease in maintenance costs, a sustainable extension of the business and a significant increase in the return of the assets.
With the purpose of standardizing and concentrating virtually in the same place all the quality audits that are carried out in production plants, we build a web platform where updates can be uploaded to see all the content online and in the same place , in addition to generating automated alerts on different platforms in the case of a critical event.
This brought as a main benefit the generation of new quality indicators and the monitoring of the plants in real time.
In order to reduce maintenance and the costs that are implied in solar panels, we developed an online platform for monitoring I-V tracers.
Through the monitoring of these tracers, the current status of the photovoltaic panel can be seen individually, thus our software works as a real-time scanner and provides automatic alerts when a problem is detected.
The main benefit of this was the generation of new quality indicators, and a significant increase in asset performance.
Instacrops App shows agricultural growing seasons and their respective current data that is updated minute by minute, historical data, wich is collected in database in order to be displayed in a graphical interface.
The app also generates reports and preventive alerts, wich become essential tools for decision making and an efficient resource for farmers.
To make decisions based on facts, Forecast’s Data Science team created analysis models based on machine learning for different types of clients and customers leak prediction based on past datasets.
Our clients were given different evaluation metrics, such as the lrak risk score and customer purchase regularity, so that their actions taken in the future are based on multiple factors considered to achieve the most accurate model.
Seeking to re-attract escaped customers and increase demand based on digital marketing, we designed a segmentation, demand prediction and customer re-acquisition system,.
For this, we first made a prediction of national demand of more than 1MM clients based on analysis of new consumer behavior data such as weather, economic indices, and others, and then segment them according to different parameters such as consumption, age, trends, etc.
The next step was to automate SMS shipments with personalized promotions at key times according to the type of target that had been previously generated. This brought as a main benefit new revenues at the SMS level, visibility, higher demand and a significant decrease in operational costs.
In order to optimize and automate the automotive fraud scoring, we generated a new high-precision Scoring using all existing variables and business rules in the evaluation process. Achieving fraud behavior predictions based on historical data per customer.
We achieve this by creating models through the integration of Data Science and Machine Learning using both historical and new critical data that we generate in the process of integrating our service. This brought as a main benefit the rapid generation of ROI, prediction of fraud behavior based on historical data per client and decompression and optimization in internal areas of the company.
Dober is a platform that integrates blockchain technology to manage digital capital in a simple, secure, reliable, and decentralized way. It consists of the implementation of smart contracts for the distribution of the ether on the Ethereum network, with it’s own token, ERC-20, in order to tokenize the participation in each one.
Dober platform makes possible to have several instances of capital accumulation pools and automated systems for their distribution.
Endangered Tokens (ENTS) is a project of e-token developed by Forcast that integrates blockchain technology to manage digital capital in a decentralized way. It consists of video-art tokens which are the main cryptoactive within the ENTS ecosystem. The token represents a specific specimen of an endangered species. If you own this token, you become the godfather of that only living being, promoting it’s protection through certified local NGO’s.
Endangered Tokens generates smart contracts to trade tokens and NFT’s, under the ERC 20 and ERC 721 protocols respectively, promoting the ownership of digital assets.
In order to optimize energy production in solar plants, we designed a web platform to process images and obtain graphical results.
In this platform, thanks to the image processing modules developed by Forcast, the detection of hot spots in the solar panels was achieved using thermal and infrared cameras, in addition to the detection of cracks, dents, dirt and dust with HD cameras and microcracks. by micro-luminescence.
This led to an improvement in the estimation of energy generation of the plant, as well as in the taking of preventive actions, and a better prediction of the damage and useful life of the solar panels using AI.
Due to the large number of cables in the poles of the metropolitan region and the risks that this can cause, we designed a web platform for a geographic information system of detected events, where, through image processing modules developed by Forcast, failures on poles and transmission lines are detected through HD cameras, performing an automatic and intelligent analysis of videos and images to alert, warn and geolocate faults.
With the aim of improving the efficiency of payment boxes in the stores, we designed a Real-time Monitoring Web Platform, where images of how many people are in line are processed to convert it into an estimate of the waiting time for 200 pool of boxes.
We achieve this by connecting to the security cameras and integrating with the VMS system in order to apply our detection modules, generating alerts in real time for more efficient personnel management and obtaining new KPIs to make decisions based on facts.
In order to optimize the review of incidents found by security operators, we carry out an intelligent camera monitoring system.
Through Forcast’s Image Processing Suite platform, critical events are filtered in an automated way to alert when one is detected, for example, unauthorized entry. In this way, the operator only needs to be attentive to critical events detected by the system.
In addition, thanks to machine learning, the system reduces the detection of false positives, which improves the operator’s performance and allows him to control a greater number of cameras.