Leveraging AI to Create Relevant Business Functionality | CEO Ben Engber Explains

At Lineate, we’re often asked how our approach compares to out-of-the-box AI solutions. The answer is simple: we leverage and extend AI and machine learning models to create relevant business functionality. Most generative AI models people talk about are trained on the internet at large and rely on that generic knowledge, plus whatever you provide in a prompt, to generate answers. For anything beyond very basic use cases, organizations need additional integration and configuration to make AI truly business-aware.

AI Systems Integration
Every organization needs to ground these tools in their own data. This means building retrieval pipelines (RAG and beyond), structuring data correctly, and integrating AI into business processes.

Synthetic Data for Sensitive Use Cases
In many industries, such as finance or healthcare, the data is proprietary or highly sensitive. You can’t simply share patient records or internal credit-decision data with an engineering team or a generative AI vendor. Synthetic data offers a solution by creating safe, structurally accurate datasets that mimic real data behavior without broadly exposing sensitive information.

 This is where AI moves from an “interesting demo” to delivering measurable business impact. In this video, Lineate CEO Ben Engber explains how Lineate leverages and extends AI models to create meaningful business functionality.