Quantum Distribution Generator, Product Announcement

Stop Hand-Rolling Your Monte Carlo Engine

Every quant desk, actuarial team, and data-science group hits the same wall: the model is ready, but the sampling engine behind it is a pile of hand-written random-number code that nobody fully trusts and that lives nowhere near the pipeline actually making decisions. Reproducibility slips, entropy quality is a guess, and every new simulation means another afternoon wiring up distributions from scratch.

The Quantum Distribution Generator hands that whole job to a single call. It is a statistical sampling and stochastic simulation engine, published as a connector on the AgentPMT marketplace so any agent can discover it and reach it straight through AgentPMT's dynamic MCP server. There is no math library to stand up and no in-house random-number service to babysit. You describe the distribution you need; it returns the draws.

In practice it covers the distributions practitioners actually reach for: exponential for wait times and decay, Poisson for arrival and count events, binomial for successes across fixed trials, beta for proportions and conversion rates, and gamma for skewed and reliability data. It runs multi-dimensional Monte Carlo sampling for simulation and analysis, and it walks random paths across one or many dimensions for stochastic-process work. Every draw can be sourced from true quantum randomness or a fast pseudo-random generator, so you choose between certified entropy and raw throughput on a per-call basis. Billing is pay-per-use with no required subscription: a team running one risk report a month pays for one risk report a month, while a desk running thousands of pricing simulations a day pays only for what it samples.

The sampler on its own is only half the value. What matters is what it feeds. Take portfolio risk: a webhook fires the moment a new position lands in the book, the flow pulls the exposure, runs a multi-dimensional Monte Carlo pass to estimate Value at Risk and tail losses, and drops the result into an investment memo before the analyst has opened a spreadsheet. Or reliability engineering on a factory line: a scheduled run each night samples gamma-distributed failure times against the latest sensor readings and flags any machine drifting toward its maintenance window. In life sciences, a clinical-operations flow can simulate patient enrollment and event rates with Poisson and beta draws to stress-test a trial design before a single site opens.

Because the generator is a marketplace connector, it chains into any of these flows and triggers itself automatically, on a schedule or from a webhook when something happens on another platform, whether that is a new deal, a fresh dataset, or a completed batch. You can drive it from Hermes, OpenClaw, Codex, Claude Code, or ChatGPT, or let it run fully autonomously with no local agent setup at all through the dynamic MCP. The simulation stops being a notebook someone has to remember to open and becomes a step that simply happens.

This matters because quantitative work has quietly become the backbone of financial services AI and the modeling that healthcare AI companies depend on. What sits under that work, though, has stayed stubbornly manual. Analysts still export to a notebook, run a simulation by hand, paste the numbers into a report, and repeat the whole ritual next month. The hours lost are the smallest part of it. Models never get re-run, risk scenarios go untested for lack of time, and numbers that were current three weeks ago quietly decay. A sampling engine that any agent can call on demand, and that plugs straight into automated financial services AI and life-sciences workflows, turns stochastic simulation from a scheduled chore into background infrastructure.

Sampling should be the easy part of quantitative modeling. Point your next automation at the Quantum Distribution Generator on the AgentPMT marketplace and let the draws come to you.