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Our Services
Cognome has developed dozens of models with multiple partners
Cognome has built and deployed over two dozen AI/ML models into production both alone and with world-class partners (like Intel). Whether you are looking for a simple Machine Learning model that will take your data and make predictions, or something more sophisticated that requires the use of Large Language Models, Cognome has “been there and done that”.
Privacy and security are paramount
We have been doing data de-identification for years. Whether it’s names, IDs, address, or any identifiable information, our de-identification LLMs remove ALL identifiers from ALL data ALL the time. And if you use our platform we have the ability to do this on-the-fly in real time, or to store it to a record permanently.
Do you have to normalize your data to make it accessible?
You may think you want your data “normalized” for your staff, researchers, or AI models, but actually you don’t. What you need is an infrastructure that can understand all of your raw data in its richest possible manifestation. Too often normalization process dumb down or simplify data so that nuances are missed. Cognome’s platform can ingest any data of any type and make it queryable and available for AI in real time. Without simplification - which retains all the nuances needed by clinicians, researchers, administrators and machines.
Don't waste your time with models that haven't been clinically validated
At Cognome, we never validate a model in isolation. From the very inception of model ideation, our data scientists work in close collaboration with a clinical champion. This champion is integral to every stage of the model validation process. It begins with identifying clinically relevant data and leveraging real-world insights to guide the machine learning model. As the data scientist develops the model and refines the performance metrics, we align these metrics with the clinical workflow.
Upon determining the appropriate metrics, we deploy our model quietly using real production data, exposing the outcomes to a select group of key stakeholders to evaluate the potential impact on their clinical processes. Once the model is deemed fit for use and we secure leadership buy-in through evidence generation. We proceed with the necessary steps to integrate it into the clinical workflow, ensuring it is most beneficial to the care teams.
After deployment and integration, we initiate an ongoing monitoring process to track model drift, performance, and clinical impact, ensuring continuous alignment with the evolving clinical environment.
It is *imperative* that your models be validated in this way as well. There are too many “me too” companies in this world who have built models based on static data sets that are available for purchase. These models will not perform well in the real world. Furthermore, model validation is never over. Any AI/ML model worth its salt will be feeding on results to continue to improve its predictions. All of ours do.