AI-native research and decision infrastructure

Turn data and evidenceinto validated decisions.

QEV is an AI-native operating system for researching ideas, testing models, managing risk, and moving validated decisions into controlled execution.

Starting with quantitative finance: strategy research, backtesting, paper trading, risk validation, and broker execution in one connected workflow.

Join Early Access Explore the System

Evidence-to-execution cycle

QEV/OS · v0

01The problem

Trading tools are fragmented. Decision systems should not be.

A strategy may begin in a notebook, move into a separate backtester, use another service for paper trading, and rely on custom scripts for execution. The evidence, assumptions, code, results, and live behavior become disconnected.

Today that workflow is assembled by hand from separate tools for:

research
data
notebooks
backtesting
broker APIs
deployment
monitoring
AI assistants
risk controls

QEV keeps the complete decision history connected — what was tested, why it was tested, what evidence supported it, and what happened after deployment.

02The operating cycle

One continuous evidence-to-execution cycle.

Every stage feeds the next, and every stage writes back to the same record. Nothing is lost between the notebook and the order.

Discover

AI-assisted research over market data, papers, news, and user datasets.

Design

Convert ideas into explicit hypotheses, models, strategies, and test plans.

Validate

Run historical, out-of-sample, walk-forward, cost, and stress tests.

Simulate

Operate strategies against live data in controlled paper environments.

Execute

Deploy approved strategies through broker-neutral execution adapters.

Monitor

Track performance, drift, exposure, failures, and the evidence behind every decision.

03Product layers

More than a backtester.

QEV is a layered platform. Each layer has one job; together they carry a decision from question to controlled execution.

Intelligence

Research agents, paper analysis, hypothesis generation, model explanation.

Validation

Backtesting, robustness checks, bias detection, confidence and evidence scoring.

Quant

Strategies, portfolios, risk models, simulations, analytics.

Execution

Broker and exchange adapters, approval policies, order controls.

Memory

Datasets, experiments, assumptions, provenance, versions, outcomes.

04First vertical

Built first for quantitative markets. Designed for broader decisions.

Enter the Quant Lab

Quantitative finance is the ideal proving ground for QEV: noisy data, measurable outcomes, strict risk constraints, and continuous feedback. Mistakes are expensive, and results are measurable — exactly the conditions a validation system is built for.

The same research, validation, and controlled-execution architecture can later support other evidence-intensive domains: forecasting, investment analysis, asset evaluation, operational decisions, and scenario simulation.

Quant is the proving ground. Decisions are the platform.

05Control model

AI proposes. Deterministic systems control.

QEV uses AI to read papers, generate hypotheses, write experiments, and analyze failures. Risk limits, approvals, position sizing, and order execution stay outside the language model.

Promotion lifecycle · every gate requires explicit approval

01AI cannot bypass position limits or risk rules.
02Live deployment requires explicit human approval.
03Every order has a traceable origin — hypothesis to fill.
04Strategies move through controlled stages, never directly to production.
05Emergency-stop and maximum-loss controls are mandatory, not optional.

06Builder philosophy

Build first. Learn theory when it unlocks the next capability.

QEV is designed for builders. It connects theory to an actual problem: improving a test, correcting a risk assumption, explaining a failed strategy, or making execution safer. Knowledge arrives in context, when it is needed.

01Build
02Test
03Find failure
04Learn the missing concept
05Improve
↺ repeat

07Integrations

Use the infrastructure that already works.

QEV is not a replacement for QuantConnect or QuantRocket. It sits above quant engines, brokers, and data sources — connecting them into one decision record.

Research engines

LEAN
QuantConnect
Custom Python
QEV-native engines

QEV sits above quant engines; it does not replace them.

Brokers

Tradier
Interactive Brokers
Webull
Futu

Tradier is the first adapter target. Others are roadmap items.

Data

Broker feeds
Licensed datasets
User-supplied data
Research documents
Alternative data

Every dataset carries provenance and version history.

Integration roadmap — availability varies by development stage. No integration is claimed live until it is implemented.

08Roadmap

What exists, what is next, what is further out.

Scope is stated plainly. The first working system is a connected prototype — not a finished platform.

Stage 1

In development

Connected prototype

Real-time market data
Strategy execution loop
Local paper simulation
Tradier broker adapter
Risk controls
Dashboard and logs

Stage 2

Planned

Research platform

Historical datasets
LEAN / QuantConnect connection
Experiment tracking
Strategy registry
Walk-forward testing
Model comparison

Stage 3

Planned

AI quant lab

Research agents
Paper-to-code workflows
Automated test generation
Evidence scoring
Strategy review agents
Controlled promotion pipelines

Stage 4

Exploring

Decision operating system

Broader asset research
Business and operational models
Cross-domain decision workflows
Enterprise deployments

Full roadmap →

Early access

Build decisions that can survive contact with reality.

Join the early-access list for product updates, prototype access, and future research partnerships.

Join QEV Early Access