Build, deploy and operate generative quantitative models across finance, science, engineering and complex systems—with governed data, reproducible execution and measurable outcomes.
Use LargeQuant’s deterministic public tools immediately. No external LLM API is required, every job produces inspectable quantitative evidence, and your first three device jobs are free.
Classify material numbers, distinguish dates and identifiers, preserve units and magnitudes, reconcile independent evidence and issue a signed claim-level verdict.
Turn dated cash drivers into base, upside and downside scenarios with explicit assumptions, liquidity warnings, model comparison and outcome tracking.
Move from governed data to model execution, composition, benchmarking, deployment and observed outcomes without losing lineage.
Execute versioned quantitative models with bounded resources and signed run records.
Operate → 02Design, train, validate and package statistical, neural, optimisation and hybrid models.
Build → 03Govern identity, versions, lineage, approvals, deployment state and retirement.
Govern → 04Join models, tools, data and rules into reproducible quantitative systems.
Compose → 05Place workloads across local, cloud, accelerated and federated infrastructure.
Scale → 06Run evidence-linked experiments, simulation loops and autonomous research.
Discover →LQMs and LLMs are complementary. The language layer can translate intent and explain results; the quantitative layer preserves numerical state, method, constraints, evidence and measurable performance.
Compare LQMs and LLMs →LargeQuant produces forecasts, probability distributions, simulations, risk surfaces, optimised allocations and decision policies—then evaluates them against constraints and reality.
Every stage produces inspectable state, not an opaque hand-off.
The same governed model lifecycle supports domain-specific data, methods, constraints and evidence.
Forecasting, risk, capital, liquidity and portfolio decisions.
↗Life sciencesMolecular systems, trial evidence, biological models and discovery.
↗Engineering & industrialDigital twins, optimisation, reliability and process control.
↗Climate & energyPhysical systems, demand, networks, scenarios and resilience.
↗Aerospace & defenceMission modelling, complex systems and bounded decisions.
↗Research & academiaReproducible computation, experiments and scientific evidence.
↗Evaluate model correctness, reproducibility, robustness, grounding, calculation integrity, calibration, constraint adherence, efficiency and observed outcomes across explicit execution modes.
A standard, tenant-scoped adapter contract connects quantitative workflows to data, finance, research, industrial and developer systems—with separate read/write authority, lineage, retries, idempotency and health.
Explore integration patterns →Run models, compose systems, execute benchmarks, verify outputs, enforce guard policies, retrieve evidence and record outcomes through shared platform services.
POST /api/v1/models/{model}/runs
Idempotency-Key: run_01J...
{
"input": { "horizon": 13 },
"evidence": true,
"mode": "standard"
}
202 AcceptedQGI is LargeQuant’s research programme for systems that can reason, transfer and operate across quantitative domains. It is a measurable frontier—not a claim that general intelligence has been achieved.
Concise definitions for decision-makers, developers, researchers, search engines and answer systems.
A Large Quantitative Model, or LQM, is a governed model system built for structured numerical computation and measurable quantitative outputs such as forecasts, simulations, risk estimates and optimised decisions.
LargeQuant builds, deploys and operates Large Quantitative Models. Its platform combines runtime, foundry, registry, composition, compute, governance and research with verification, benchmarking, guardrails, evidence and outcome measurement.
An LLM is optimised for language. An LQM is optimised for quantitative state, explicit numerical methods and measurable outcomes. They can work together while the LQM remains the authority for the numbers.
Quantitative Generative AI produces structured numerical artifacts such as forecasts, distributions, simulations, scenarios, risk surfaces and optimised decisions rather than treating prose as the numerical source of truth.
No. A signature proves packet integrity and input binding. Correctness depends on the quantitative verdict, source quality, method, uncertainty and observed outcomes.
Yes. LLMs and agents can call LargeQuant through REST and MCP while LargeQuant preserves numerical authority, policy checks and evidence.
Start with the platform, evaluate with LQBench or add quantitative assurance to an existing AI system.