Quantitative Language Models

Language orchestration for governed financial intelligence.

A FinanceGPT QLM combines language inference, financial context, quantitative models, data, tools, workflows and governance. The language layer interprets intent; FinanceGPT quantitative services perform the calculation.

Start with FinanceGPT API docs

QLM composition

Language runtime

Cloud, private or governed model endpoint

Financial context

Workspace evidence, documents and connected data

LQM modules

Forecasting, valuation, risk, simulation and optimization

Tools & workflows

MCP tools, workflows and approved operations

Governance

Data boundary, action boundary, approvals and audit

Compound financial AI

One request can invoke several governed layers.

User intentNatural-language financial question or workflow request.

QLM runtimeInterprets context, permissions and required tools.

LQM & dataRuns quantitative models against governed financial evidence.

ControlsApplies data boundaries, approvals and execution policy.

ResponseExplains results with calculations and evidence attached.

Provider neutral

Choose the inference boundary.

FinanceGPT can route QLM language inference through configured cloud or private endpoints while retaining a separate quantitative and governance layer.

Quant aware

Use models instead of prose arithmetic.

QLMs can invoke registered quantitative modules for valuation, forecasting, risk, simulation and portfolio analysis rather than asking a language model to fabricate calculations.

Governed

Action authority is explicit.

A QLM's allowed tools, workflows, data egress and action boundary are configured independently from its ability to explain or recommend.

QLM Studio

Compose a financial intelligence profile around your own data and mandate.

Select a language runtime, quantitative modules, workspace evidence, MCP tool grants, workflows, data boundary and action boundary. QLM configuration remains versioned and governed.

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