We craft solutions powered by data and AI guiding organizations from the initial idea through to the final product. Our expertise extends across the entire development process, from concept design and prototyping to final production. This spans everything from developing recommendation systems and optimizing pricing strategies to forecasting demand and implementing predictive maintenance.
Client success story: Enexis Netbeheer
One of the largest distributors of the electricity and gas grids in the Netherlands has partnered with Xomnia to create 4 models to predict the areas that have a high likelihood of causing big disturbances on the electricity grid in the future. Our partnership also helped the client future-proof its grid in the face of any overload that can potentially disturb its customers.
Monitor trends, track key metrics, and make informed decisions swiftly with dynamic, interactive dashboards that transform complex data into clear and intuitive visualizations.
Drive strategic decisions with predictive modeling
Leverage your historical data to forecast outcomes, and stay ahead of the curve by predicting future trends and customer behaviors. Tailor your approach with customer segmentation, and craft personalized marketing campaigns that resonate and convert.
Personalize your approach with deep neural networks
Open the door to genuine, meaningful interactions and make every customer feel understood and valued with recommendation systems and natural language processing applications. Precisely suggest what your users seek and increase your customer satisfaction and loyalty.
Identify the project's use case and develop a proof-of-concept (POC) to showcase potential solutions, involving data analysis, feature engineering, and model development. Validate the proposed solution's feasibility.
2. Productionalize data product
Evolve the POC into a minimal viable product (MVP) by refining the model, setting up necessary infrastructure, and ensuring system integration. The MVP should be a functional solution ready for real-user testing in a production environment.
3. Scale & optimize data products
Expand the MVP to accommodate the increased workload and enhance the model's performance. The objective is to ensure that the solution meets intended usage demands and delivers continuous business value.
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