AI innovations have long promised productivity at scale, powered by breakthroughs in underlying technologies such as large language models (LLMs), aiding state-of-the-art applications to reason with remarkable fluency. Yet as AI adoption deepens, the constraint is no longer what models can do, but the data science and analytics foundations they depend on. Across industries, the data infrastructure feeding modern AI still resembles digital filing cabinets. Critical information remains scattered across platforms and disconnected tools, while data reaching sophisticated models is often stripped of context before inference begins.
In 2025, that realization created momentum for a new class of companies focused on making enterprise data usable at scale. Unstructured is streamlining fragmented enterprise documents, transforming PDFs, slides, emails, and other unstructured content into context-preserving inputs that AI agents can reliably reason over. In regulated industries, Basil Systems and 3E are reframing compliance and safety as large-scale data unification challenges, aggregating hundreds of millions of records to surface risk signals earlier and with traceable evidence.
Many AI systems still rely on batch data ingestion, introducing delays that limit responsiveness in fast-moving environments. Chalk is collapsing the distance between notebook experimentation and millisecond production inference, while Statsig is code experimentation frameworks directly into the software development lifecycle so that every product change becomes measurable at deployment. Feedzai is transforming risk detection through its data orchestration layer and federated learning that enables banks to collaborate against financial fraud without exposing customer data.
Underneath these shifts, the infrastructure powering data systems is also being re-architected. DataPelago is redesigning execution engines to utilize GPUs across analytics and AI workloads, and Pravāh is modeling electric grids as living graphs that learn continuously from fluctuating renewable inputs. Synchron and Pathway are extending this shift, building neural data interfaces and AI architectures that can learn and adapt continuously after deployment.
1. Unstructured
For standardizing preprocessing of unstructured data for agentic AI
For all the progress in generative AI, most enterprise systems still struggle with a basic mismatch: modern AI models reason fluently, but the data they depend on arrives fragmented, flattened, or stripped of context long before inference begins. Unstructured transforms real-world documents—PDFs, slides, emails, scans, and reports—into data that enterprises can reliably use in retrieval, search, and agentic AI systems. In the past year, the company has expanded beyond document parsing into a full preprocessing layer for generative AI.
Its platform now supports 68 file types, more than 30 enterprise connectors, and layout-aware transformations that preserve structure, hierarchy, and meaning. A new auto-orchestration system dynamically selects the optimal processing strategy on a page-by-page basis, balancing cost, speed, and accuracy without manual tuning. And a new Model Context Protocol server has further embedded Unstructured directly into AI workflows, enabling models and agents to process data using natural-language commands. The platform is now relied on by 82% of the Fortune 1000 and is deployed across commercial and public-sector environments where reliability and compliance matter most. As AI systems become operational dependencies rather than experiments, Unstructured is making data readiness a solved problem.
2. Basil Systems
For transforming fragmented life sciences data into AI-driven intelligence
