Gradient Deep Research · Global Agent Network
A research desk as wide as the market.
Ask it anything — a market-wide screen, a pointed question, a single ticker. Autonomous agents read filings, fundamentals and news across thousands of global equities, around the clock — roughly 500× the coverage of a human analyst, at the depth of one. Every answer is researched against a point-in-time database, then turned into forecasts and clear recommendations. The breadth is the edge: Sharpe scales with the square root of independent positions.
Live network view. Activity figures shown are illustrative.
Why breadth wins
Sharpe scales with the square root of breadth.
IR ≈ IC × √N — skill × √breadth
You just watched thousands of agents work a single market in parallel. That width — not any one brilliant call — is the edge, and a law says why. The fundamental law of active management (Grinold, 1989): risk-adjusted return is skill times the square root of the number of independent positions. A human desk can only add names by diluting depth. Our agents hold depth constant and scale the count — the same per-name skill applied to 100× the breadth compounds into 10× the Sharpe ratio.
Sharpe multiple vs. independent positions
EQUAL SKILL PER NAME (IC HELD CONSTANT) · INDEXED TO A 30-NAME DESK · LOG SCALE
VIEW AS TABLE
| Independent positions | Sharpe multiple |
|---|---|
| 10 | 0.6× |
| 30 — human desk | 1.0× |
| 100 | 1.8× |
| 300 | 3.2× |
| 1,000 | 5.8× |
| 3,000 — Gradient fleet | 10.0× |
Breadth only counts when the positions are genuinely independent — so the common factors (market, sector, size) are stripped out first, leaving each bet to stand on its own residual. Illustrative relationship; assumes constant information coefficient and faithful transfer of forecasts into positions.
Agent output · verbatim
Watch one agent reason.
Breadth is only worth 10× if every one of those thousands of positions is real research — not a guess. So here is the reasoning a forecast and a recommendation are built on: every finding runs the same fixed contract, traceable end to end, with absolute neutrality. Step through three from one cluster run:
See it on your own names
Point the fleet at a company you know.
The fastest way to trust a research desk is to test it where you already have a view. Screen the whole market, drill into a single name, or let it monitor your book overnight — early access runs the same agents you just watched, on the names that matter to you.
Ask it anything
A ticker, a market-wide screen or a pointed question — across 15,000+ covered equities.
The fleet researches it
An agent forms a hypothesis, interrogates a point-in-time database and self-corrects — zero look-ahead, absolute neutrality.
evidence → a forecast with a long-term value → a clear, conviction-scored call · full audit trail
↻ Then it keeps watching: every night the fleet re-runs your book and turns new evidence straight into an updated recommendation.
Read it against what you already believe. That's the test.
Gradient Deep Research · Early Access
Machine-scale research.
Human-grade depth.
Test it on the names you know best — and judge the results for yourself.
The company examples and figures on this page are illustrative — they show how Gradient Deep Research works (its research workflow, evidence trail and output format), not actual holdings, forecasts, or investment recommendations.