Relying on AI that fabricates information or pulls facts from thin air is a fast track to eroding trust, making poor decisions, and incurring significant costs.
Many business leaders assume that once an AI model is trained on a massive dataset, its outputs are inherently ‘intelligent’ and aligned with human values.
Disconnected data cripples AI applications. Businesses invest heavily in AI, only to find their models deliver shallow insights because the underlying information is siloed, unstructured, or lacks the crucial context of relationships.
Your large language model application seems to forget details from earlier in the conversation. You’ve fed it reams of context, assuming it would retain everything, but it still makes critical errors or asks for information it should already have.
Imagine a complex AI model, meticulously trained, achieving near-perfect accuracy on its development data. Your team is excited.
Building a large language model application often presents a dilemma: you need the model to perform a specific, nuanced task, but fine-tuning it with thousands of examples is too slow, too expensive, or simply not feasible for a proof-of-concept.
Many business leaders and technical teams express frustration with large language models. They invest in LLM tools, craft what seems like a clear prompt, yet the output often falls short.
Many executives hear terms like “AI agent” and “AI assistant” used interchangeably, blurring crucial distinctions that impact strategic investment and operational outcomes.
Most businesses today grapple with fragmented insights, relying on AI systems that process data in silos. Your customer service AI might analyze text chats, while your sales analytics focuses on purchase history, and your operational sensors log machinery data.
Deploying powerful AI models directly onto edge devices — think industrial sensors, smart cameras, or embedded medical devices — often hits a wall.
