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QSentia company
A company built around evidence before allocation.
QSentia exists to make AI and systematic trading agents easier to evaluate with discipline. The platform connects backtest evidence, live paper telemetry, broker status, commercial access, and investor workflows so allocation decisions start from verifiable records.
Firm thesis
Agent investing needs proof, operating controls, and monitoring investors can understand.
Positioning
Evidence layer for agent allocation
A company focused on making systematic agent performance, telemetry, and access states inspectable.
Launch focus
Discovery and Shadow Mode
Public diligence, paid evidence access, and monitored paper capital paths before funded allocation.
Operating posture
Evidence before allocation
Every performance claim should map back to source rows, live status, trace logs, and explicit missing states.
Founder-market fit
From AI research to accountable agent-market infrastructure.
QSentia was founded by Lucas to make systematic, machine-learning-based agent evaluation more transparent for professional investors. Lucas is a doctoral researcher in machine learning and artificial intelligence with applied data science, finance, economics, and enterprise AI experience.
The company is being built as infrastructure first: agent registry, backtest evidence, live broker telemetry, Shadow Mode simulations, investor access control, and audit-ready operating records. The goal is to make diligence, monitoring, and access control part of the product, not an afterthought.
Operating process
How QSentia turns agent research into an accountable marketplace.
Research is versioned
Each strategy starts with a defined research source, imported backtest evidence, documented assumptions, and production artifacts.
Agents are published with context
The marketplace presents return, Sharpe, drawdown, win rate, evidence logs, broker status, and subscription readiness in a consistent format.
Live behavior is monitored
Paper and live agent telemetry is separated from historical backtests so investors can see current account behavior without confusing it with simulation data.
Access is commercially gated
Discovery, Shadow Mode, and future allocation workflows are controlled by account status, payment state, onboarding, and operational review.
Governance principles
Built to separate claims, capital, and live telemetry.
QSentia is designed as a software and agent-intelligence platform. Funded allocation workflows remain subject to onboarding, suitability, account controls, payment state, and operational review.
Keep backtest evidence, live telemetry, and commercial access states separate.
Capture account baselines before live portfolio performance is calculated.
Use isolated broker credentials and account-specific monitoring for production agent lanes.
Show pending or unavailable data directly instead of filling investor pages with fallback metrics.
Route agent publication, risk review, and customer access through explicit operating controls.
Treat every performance claim as a source-backed record that can be inspected.
Company focus
The platform is the product.
QSentia is not just presenting agent performance. It is building the commercial, operational, and investor-facing system around agent access.
Agent Evidence
Backtest summaries, trade logs, orders, positions, drawdown traces, and methodology context.
Live Telemetry
Portfolio returns, broker health, execution traces, orders, fills, and monitored account status.
Investor Access
Discovery access, Shadow Mode capital tracking, payment-gated subscriptions, and allocation readiness.