Quantitative intelligence for financial decisions that must survive scrutiny.
LargeQuant connects governed data, quantitative models, simulation, optimisation, uncertainty, benchmarking and decision evidence for teams operating where numerical assumptions must remain inspectable.
The model output is only the beginning.
Financial institutions operate with layered model, data, risk and governance obligations. A forecast, exposure, optimisation or stress result becomes materially more useful when the organisation can reconstruct the information set, model state, constraints, uncertainty and decision rule that produced it. LargeQuant provides that operating layer around quantitative work.
High-value quantitative workflows.
Forecasting & planning
Build time-series research workflows with versioned data, benchmark comparisons, uncertainty and realised-outcome reconciliation.
Portfolio & allocation
Optimise allocations under explicit constraints and retain the numerical basis for candidate selection and decision policies.
Risk & stress analysis
Run scenarios and quantitative simulations while keeping assumptions, distributions and evidence attached to the result.
Model benchmarking
Compare production or research models against challengers and baselines using repeatable evaluation definitions.
Model review & governance
Preserve dataset lineage, model identity, evaluation history, reproducibility packs and decision evidence for review.
Quantitative research operations
Coordinate studies, experiments, model runs and outcomes without losing the lineage between research and deployment.
Connect the domain model to the evidence and decision layer.
Quantitative infrastructure around your domain expertise.
Data lineage
Version datasets and retain source/evidence references.
Forecasting & time series
Support quantitative temporal modelling and evaluation workflows.
Simulation
Represent scenarios and distributions rather than only single-point predictions.
Optimisation
Solve bounded allocation/decision problems under explicit constraints.
LQBench
Preserve benchmark methodology, metrics and evidence for model comparison.
Reproducibility
Bind data, model, protocol and software state to the resulting quantitative evidence.
Designed to connect to the stack you already operate.
- Market and portfolio datasets
- Risk engines and quantitative model outputs
- Internal data stores and approved APIs
- Research notebooks or model-development environments
- Decision/risk limits and governance workflows
Built for cross-functional quantitative work.
- Quantitative Research
- Model Risk Management
- Portfolio & Investment Engineering
- Treasury / ALM
- Risk Analytics
- Data & AI Platform Teams
Keep the basis of the decision inspectable.
LargeQuant keeps prediction, simulation and realised outcomes distinct. That makes it possible to reconstruct what was known when a decision was made and to compare that decision with what happened later.
Explore evidence & provenance →