◆ Stealth · launching 2026

Agentic Alpha.
Asymmetric edge.

Agentic Alpha(AA) Quant is an AI-native Quant fund. Coordinated agent swarms run on proprietary alt-data pipelines to find alpha in small-cap niches that billion-dollar funds structurally can't touch.

Updates only when there's signal worth sharing.
100+
Proprietary data sources
100×
Cheaper than institutional alt-data
Small-cap
Where institutions structurally can't fit
2026
First live track record
Our edge

Most AI funds buy commodity data and run a single LLM analyst.
We don't.

Three things compound: where we look, how we read, what we run on.

01 · Where we look

Proprietary alt-data

100+ free + scraped + LLM-extracted sources institutions don't bother with — federal contracts, FDA actions, foreign filings, niche community signals, FCC pre-launch filings, real-time grid load. The aggregation effort IS the moat.

02 · How we read

Agent swarm extraction

Frontier-LLM agents debate adversarially across cross-provider models. Risk-factor diffs, CFO tone shifts, supplier graphs, insider quality scoring — interpretation that takes a 20-PhD team or a swarm of agents. We picked the agents.

03 · Where we trade

Small-cap niches

Billion-dollar funds need $10M+ position sizes. We trade where they structurally can't fit — undercovered small-caps, capacity-capped strategies, compliance-restricted data sources. Every constraint is a moat.

Where the edge is

We compete where institutions structurally can't.

$1B+ funds need $10M+ position sizes. They can't fit in the small-cap niches where insider quality, cross-language filings, and weird alt-data still produce real, defensible alpha.

Dimension
Most AI funds
Agentic Alpha(AA) Quant
Data sources
Commodity vendors ($30K-$100K/mo)
100+ free + scraped + LLM-extracted, aggregated at scale
LLMs in the loop
Single LLM analyst
Adversarial swarm with cross-provider debate
Strategy generation
Human-coded, AI-suggested
Agents auto-generate, backtest, deploy
Explainability
Black-box ML
Evidence chain per trade, audit-replay, prompt versioning
Where we trade
Liquid large-caps (saturated)
Small-cap niches institutions can't size into
Compliance posture
Restricted to vendor-cleared data
Free to use Reddit, Polymarket, niche scraped sources
Why now

Four curves crossed in 2026.

The frontier moved. Sophisticated alt-data alpha used to require a $5M data budget and a team of 20. It now requires engineering effort and a frontier-LLM API key.

01

Frontier model intelligence

Frontier LLMs read SEC filings, classify insider quality, and detect tone shifts at near-analyst quality at fractions of a cent per call.

02

Agent infrastructure

Multi-agent orchestration, tool use, and prompt versioning are mature enough to run autonomous decision loops with full auditability.

03

Alt-data accessible

Free + scraped + LLM-extracted data now matches what institutions pay $30K-$100K/mo for. The new edge is interpretation, not collection.

04

Compliance moat opens

Reddit, Polymarket, ADS-B, niche scraped sources — all useful, none clearable by big-fund compliance committees. The small-fund advantage is structural.

Stay close

Live trading begins 2026.
Track record follows.

We don't pitch a fund without a track record. We don't share live numbers until the audit log proves them. When the numbers are real, this list hears first.