Why AI Needs Better Context

The need for robust, fresh context has never been greater as companies increasingly rely on AI to drive personalized, real-time decisions. The challenge lies in seamlessly integrating and transforming multiple forms of data: structured features, embeddings for unstructured data, and dynamic prompts for large language models (LLMs). To build a scalable infrastructure capable of meeting the demands of production applications, an advanced knowledge base is required that synthesizes structured data, semi-structured logs, and unstructured documents in real-time. This knowledge base must ensure version control, reproducibility, and optimized storage while handling enormous processing loads without sacrificing performance. Features, embeddings, and prompts serve as the pillars of AI context, providing structured signals for models to interpret specific patterns in real-world data, transforming unstructured data into dense vector representations, and defining how models interact with context at inference time. To build production-grade context infrastructure, companies need unified systems that coordinate features, embeddings, and prompts without sacrificing performance or increasing costs. Tecton's platform provides the robust infrastructure needed to compute, store, and serve dynamic AI context at scale, ensuring data freshness and reliability for both ML and GenAI applications.