AI, Generative AI and Machine Learning

AI, Generative AI and Machine Learning

Kickstart Your Data Flywheel

Generative AI is here. The only question is… are you ready for it?

Passerelle provides the expertise to go from legacy data management to AI-ready infrastructure, with Agile Data Governance programs, data quality tools, and purpose-built architecture that supports one version of the truth. We help define the right use cases and choose the appropriate technology, whether you are pursuing ML workflows for predictive analytics, Generative AI applications to better understand and utilize unstructured data or graph analytics to visualize spatial and relational data.

Passerelle’s services and offerings span the data value chain, laying a strong data quality foundation for advanced data applications with cloud migration and driving immediate value by leveraging underutilized data in predefined GenAI use cases.

Passerelle AI + ML Services

AI + ML Readiness

Results from AI and ML applications can only be as trusted as the data they use. The fundamentals of data management, data preparation and data stewardship have never been more important. Passerelle helps organizations build a scalable data management practice focused on creating a single version of the truth in the Snowflake Data Cloud. With trusted data in place, you can bring AI and ML applications directly to your data, promoting security and making insights accessible to the right people at the right time.


Generative AI can be used to mine insights from existing data sources that have been traditionally difficult to access such as PDFs, call center transcripts, contracts, and documentation, using Large Language Models (LLMs) to sift through volumes of data with interfaces that require little to no technical expertise. GenAI can have an immediate impact on customer satisfaction and operational efficiency use cases while eliminating time-consuming and error-prone manual entry and reporting on unstructured and semi-structured data.

Predictive ML

Predictive Machine Learning uses historic and third-party data to forecast future trends and behaviors, such as customer churn, product demand, and support for next-best product initiatives. Other potential use cases include mitigating losses by identifying potential fraud or financial risks and improving resource allocation with planned maintenance or optimized logistics.

Graph Analytics

Graph Analytics helps uncover relationships and patterns in complex networks to provide better insights into customer behavior, identify suspicious patterns and anomalies for fraud detection, or improve overall efficiency and performance.

Case Studies

We help businesses harness the power of their data.

National Bank