Once we have identified your company’s challenge and the strategy to solve it with AI, we apply an approach based on agile methodologies that adapts to the current state of your project. With more than 50 solutions developed, we follow a process structured in three key phases: proof of concept (PoC), minimum viable prototype (MVP) and go-to-production.
Each of these phases is independent and can be carried out separately, depending on the status of your project.
If you have identified how AI can benefit your business, it is time to confirm the technical and business feasibility of your idea with a proof of concept.
After validating your idea, we move on to the development of an MVP to test your solution with a group of users, gather their feedback and iterate before launching.
With a viable MVP iterated based on the feedback data received, we move to the production phase, preparing the solution for implementation and scalability in a real environment.
One of the most relevant technologies today are AI models that generate new and original content in text, image or audio format by learning from large sets of existing data.
Some of its applications are the generation of shadows in an image, the digitisation of expert knowledge or the automatic summarisation of meetings.
This technology is used to summarise large and complex documents, automating the creation of summaries of all types of content. It improves knowledge management in companies and facilitates quick access to relevant information, helping teams to stay aligned and make informed decisions. It also optimises efficiency in meetings by generating accurate summaries of key points discussed and next actions, as does Jotty, our digital assistant.
Generative AI facilitates the fast and precise creation of content such as texts, videos, images or audios adapted to different needs (marketing actions, knowledge database…). One example is the project we are carrying out with Michelin, to concentrate all the knowledge of its production plants, making it easier for employees to consult information from other plants, to answer their questions about their processes and to manage information easily.
Systems that make use of this technology help automate manual tasks that take up a lot of a company’s time and resources. They are used to automate operations in factories (where they can use data from IoT devices) or other processes such as photo retouching, as achieved by Micolet and which resulted in cost savings of 10%.
Recommendation systems suggest products, services or content to the user, based on the analysis of their preferences and those of similar users.
They help to hyper-personalise the experience, achieving greater loyalty and better customer conversion.
Recommendation systems personalise the online experience by analysing user behaviour and preferences to suggest products or services aligned to their interests, thus improving conversion. The system we developed for an anime platform helped this company secure a constant and loyal portfolio of premium customers, ensuring the viability of its business model.
Through recommendation systems and other technologies, companies improve customer retention. They receive suggestions for new customers, identify potential churn and personalise offers and services that increase customer loyalty. In different sectors such as finance or communications, they generate exclusive promotions and recommendations, personalised campaigns and new customer propositions to reduce churn.
By analysing users’ behavioural data and usage patterns, systems with this technology offer real-time recommendations that maximise the personalised experience in different activities. For example, in the leisure sector, it is used to suggest activities and routes adapted to the user’s preferences. In education, we have developed solutions that adapt training itineraries according to the student’s progress, optimising the learning process and improving results.
This technology (NLP) allows machines and digital systems to detect, understand, interpret and respond to human language, both written and spoken.
It is essential for developing tools such as virtual assistants and voice recognition systems, optimising the interaction between people and digital systems.
This technology analyses large volumes of written documents, identifying patterns, extracting key information and understanding their context. It automates document management, reducing errors, saving time and facilitating strategic decisions in sectors where accuracy is vital. For example, the project developed for Incibe improved efficiency in the management of large databases.
Intelligent virtual assistants use NLP to understand and answer queries in natural language and allow users to resolve doubts, perform tasks, and access information instantly, improving their experience. In the case of 24h assistants, they benefit companies by reducing operational costs and increasing efficiency. This approach is present in projects such as Snorble, where the virtual assistant implemented in an IoT device understands, interacts and responds to children’s demands.
Sentiment analysis using NLP interprets text to identify users’ emotions and perceptions about products and services, providing valuable data to adjust strategies and improve customer service. This technology helps to classify problems and queries, optimising customer response and anticipating needs. One example is the project that revealed challenges in the collaborative economy where customer opinions about different services were analysed to address their pain points.
Predictive analytics uses historical data to predict future events, identifying patterns and trends and enabling companies to anticipate them.
This technology is helping companies with intelligent inventory management or predicting customer churn in sectors such as finance.
Predictive analytics adjusts prices in real time based on demand and market conditions. By analysing large volumes of historical and current data, AI predicts fluctuations and suggests optimal prices that maximise revenue and competitiveness. This allows companies to react quickly to market changes, strategically adjust margins and capture greater market share by keeping prices aligned with consumer demand and competition.
Analysis of equipment condition and performance data predicts failures before they occur, improving operational efficiency. One example is the project we undertook to predict the strength of multi-material welds. By applying AI to predictive maintenance, companies accurately schedule preventive repairs, avoid unexpected downtime and reduce operating costs, resulting in greater system reliability and better use of resources.
By analysing patterns in historical and current data, it is possible to anticipate changes in the market, helping companies to adapt strategies and make informed decisions. This ability to forecast product demand or changes in consumer behaviour creates a significant competitive advantage. By identifying these trends in advance, companies adjust their offerings, optimise their inventories and improve customer satisfaction by proactively responding to market needs.
Image or video processing (also known as machine vision) allows computers to interpret images and videos, replicating the human ability to recognise objects.
It is an essential technology in security, manufacturing, healthcare and autonomous vehicles.
Machine vision transforms security in retail stores and public places by analysing images in real time to detect unusual situations. This technology facilitates an immediate and accurate response, creating a safer environment for both customers and employees. One example is our project developed with Pose technology, which has managed to extract detailed information from customers to improve their experience without compromising privacy.
Image and video processing significantly improves quality control by performing accurate and continuous product identification and inspection. It detects imperfections that the human eye cannot see and ensures that quality standards are met. In addition, it minimises waste and optimises inspection processes in sectors such as manufacturing, food and pharmaceuticals. It can also be adopted for air quality management, as we applied in the AirLuisa project.
Computer vision improves medical diagnostics by analysing complex images such as X-rays and MRI scans, detecting abnormalities quickly and accurately. Doctors identify medical conditions earlier and with greater accuracy, improving efficiency of care. Projects such as SINUÉ have demonstrated how this technology can be integrated into clinical workflows, raising standards of care and facilitating more informed decisions.
75% of AI projects fail due to a bad approach or not having an expert team.
At Sciling we have 10 years of experience in developing AI projects. If you have already identified your organisation’s challenge or the use cases that could use AI to be solved, contact us so we can help you.
