Blogs

A B2B services company had a data problem that was eating their operational capacity. Customer records lived across five systems: Salesforce for sales, Zendesk for support, Stripe for payments, NetSuite for accounting, and a custom tool for fulfillment. Every new deal required manually reconciling data across all five, populating 150+ fields by copying, pasting, and cross-referencing. The operations team spent an average of 4 hours per deal on data entry. Not selling. Not supporting customers. Just copying data between systems that didn’t talk to each other. ...

Real estate agents have a problem: buyers know what they want, but search interfaces don’t speak their language. A buyer says “three bedroom house with a pool near good schools.” The typical real estate portal offers 47 checkboxes, 12 dropdown menus, and a price slider. When a real estate technology company asked us to solve this, they expected we’d recommend an LLM. Everyone recommends LLMs for natural language these days. We built something different, something that works better for this specific problem. ...

When a marketing team came to us pushing 100,000 emails daily with plans to scale to 10 million, they had a problem that no off-the-shelf solution could solve. HubSpot, Marketo, and similar platforms handle email well in isolation. But this campaign required real-time orchestration across five different systems: marketing cloud, CRM, data warehouse, compliance systems, and billing. The Challenge The existing workflow was a patchwork of manual processes and fragile integrations: ...

As artificial intelligence continues to evolve, we are witnessing the rise of large language models (LLMs) like ChatGPT, which have become increasingly sophisticated and useful. But did you know that connecting these powerful LLMs to other applications can pose potential security risks? Let’s dive into the hidden dangers of connecting LLMs to other applications and uncover some of the latest attack methods hackers are using, providing specific examples of such threats and explaining the underlying concepts clearly. ...

The rapid advancement of AI systems has brought both excitement and concerns. One such concern revolves around the mistakes AI models make, often referred to as “hallucinations” in academic literature. However, the term “confabulation” may be more appropriate due to its more accurate representation of the creative gap-filling principle at work in AI systems. In this article, we will explore how to minimize confabulations in AI systems through various strategies that focus on prevention, correction, and optimization. ...