Quantitative infrastructure for domains where the decision is governed by numbers.
LargeQuant provides a common operating layer around domain-specific models, solvers, datasets and experiments. The science changes by industry; the need for uncertainty, evidence, reproducibility and disciplined decision logic does not.
Markets, risk and governed model decisions
Forecasting, scenario analysis, optimisation, benchmarking, model evidence and realised-outcome reconciliation.
Explore →PHARMA & BIOTECHComputational discovery programmes
Targets, molecular campaigns, QSAR, docking, developability, synthesis planning and measured assay evidence.
Explore →MATERIALS & CHEMICALSMaterials and process discovery
Composition spaces, multi-fidelity surrogates, DFT/high-fidelity simulation and evidence-driven next experiments.
Explore →INDUSTRIAL ENGINEERINGDigital twins and reliable optimisation
CFD/FEA, HPC, calibration, surrogate modelling, uncertainty and reliability-gated decisions.
Explore →RESEARCH INSTITUTIONSReproducible scientific infrastructure
Projects, experiments, datasets, execution, benchmark publication and institution-linked provenance.
Explore →See the same Scientific OS expressed in two very different physical domains.
Biomedical research & quantitative human systems
Anatomy, observations, model state, uncertainty and scientific evidence.
STRATOMISSIONNear-space aerospace & biomedical research
Earth data, payloads, test requests, missions, reliability and evidence.
One quantitative operating layer
Models, execution, provenance, reproducibility and bounded decisions across both.
Connect domain expertise to modelling, execution and proof.
Model
Use statistical, ML, physical, molecular or solver-based numerical authorities appropriate to the domain.
Execute
Run simulations, HPC and trusted solver workflows or ingest laboratory/experimental results.
Evaluate
Benchmark models, calibrate against evidence and quantify uncertainty and reliability.
Prove
Preserve lineage, reproducibility, signed evidence, benchmark publication and institutional provenance.