Vision
The case for scientific intelligence infrastructure
AI that touches real scientific problems needs more than a model wrapper. It needs systems that can plan, execute, verify, and explain across data, compute, and knowledge layers.
The Scientific Intelligence Stack A layered architecture showing data, databases, execution, knowledge, and scientific discovery with cross-cutting trust, security, observability, governance, and reproducibility. The Scientific Intelligence Stack
Scientific AI needs a stack that can plan, execute, explain, and govern across data, models, and compute.
Scientific and Operational Data Sensors, metadata, APIs, files, publications, instruments Database and Query Systems Query planning, search, polystores, streaming, graph processing Distributed and HPC Execution Kubernetes, HPC, GPU workflows, scientific workflows, monitoring Knowledge, Models, and Agents Graphs, ontologies, LLMs, retrieval, planning, and evaluation The stack is designed for humans and agents. The system has to keep provenance visible. Execution must be observable and tunable. Discovery should remain grounded in the source data. Cross-cutting concerns: trust, security, observability, governance, reproducibility For startups
Platforms that survive real product pressure
The practical question is how to build infrastructure that can change shape without losing control of cost, correctness, or operability.
For researchers
Systems that preserve meaning
Scientific data is most valuable when provenance, semantics, and reproducibility remain visible all the way through discovery and analysis.
For institutions
Infrastructure that can be governed
Trustworthy scientific AI depends on auditable execution, policy-aware access, and technical interfaces that are legible to both users and operators.