Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments,
Maintaining model agility is crucial for organizations to adapt to technological advancements and optimize their artificial intelligence (AI) solutions. Whether transitioning between different large language
This post was co-authored with Krišjānis Kočāns, Kaspars Magaznieks, Sergei Kiriasov from Sun Finance Group If you process identity documents at scale—loan applications, account openings,
Modern enterprises face mounting challenges in extracting actionable insights from vast data lakes and lakehouses spanning petabytes of structured and unstructured data. Traditional analytics require
AI agents in production environments often need to reach internal APIs, databases, and private resources that sit behind Amazon Virtual Private Cloud (Amazon VPC) boundaries.
Researching the path to AI-augmented care and development of an AI co-clinician.
“The thing that really struck me when I came to MIT and strikes me every single day is the stuff that’s going on here is
In today’s hospitals and clinics, a dermatologist may use an artificial intelligence model for classifying skin lesions to assess if the lesion is at risk
This post was co-written with Yash Munsadwala, Adam Hood, Justin Guse, and Hector Hernandez from PwC. Contract analysis often consumes significant time for legal, compliance,
When building AI agents, developers struggle with organizing memory across sessions, which leads to irrelevant context retrieval and security vulnerabilities. AI agents that remember context