Extract alpha from the best investing minds on the internet.

X / Twitter
Reddit
YouTube

See what is worth trusting now.

AI Frontrunner ranks market calls by source history, thesis context, graph connections, and follow-up research so each idea comes with evidence, not hype.

Create account

Follow the evidence before it becomes consensus.

Explore how an asset thesis connects source history, counter-evidence, catalysts, and the next review step in one auditable view.

Illustrative
Evidence trail
Scenario review
Source links

Illustrative evidence timeline

This illustrative workflow shows how evidence, market context, and counter-evidence can be reviewed together. It is not performance data.

Signal Detected

Review Updated

Signal A

Signal B

Signal C

Reference context

HighGrowingReviewNewSignalReview

Signal

Evidence

Scenario

Review

Reference

context

Illustrative

Signal A

primary evidence

Illustrative

Signal B

market context

Illustrative

Signal C

counter-evidence

Illustrative

Turn scattered narratives into an inspectable thesis.

Research views connect evidence, impacted assets, source quality, scenarios, and explicit refutation signals so the next review has context.

Evidence-led
Primary sources
Counter-case
Next actions

Illustrative thesis workflow

AI infrastructure power bottlenecks

An example of how a theme can connect evidence, public-market exposure, and counter-evidence. This is not a live recommendation.

Evidence

Primary sources

Scenario

Base / bull / bear

Counter-case

Tracked

Exposure

Inspectable

Understand how ideas connect before you size them.

The graph maps shared themes, impacted tickers, linked ticker theses, and source clusters so related market narratives stop living in separate notes.

Connected evidence
Shared themes
Asset exposure
Related theses
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AI power

Review source quality with a complete evidence trail.

Source reviews keep evidence quality, claim history, calibration, and corrections inspectable instead of relying on follower count.

Auditable
Evidence quality
Claim history
Outcome review
#1

Evidence quality

Primary sources

Inspect

#2

Claim history

Timestamped

Review

#3

Outcome score

Methodology

Audit

#4

Corrections

Versioned

Track

Connect trusted financial evidence to the agent you control.

The platform is building provider-neutral API, CLI, and MCP access around bounded capabilities, provenance, and durable usage receipts.

Gated preview
Capability catalog
Bounded reads
Usage receipts

Inspect public capability catalog

Read-only

Available

Connect a revocable agent credential

Gated

Preview

Record bounded workspace operations

Receipted

Planned

Explore the public proof layer

Start with the live research preview, thesis graph, public author, ticker, and ticker-thesis pages, then move into the public API docs. These surfaces are designed for discovery, SEO, and proof: who made the call, what they said, how it worked out, how theses connect, and how to integrate the data programmatically.

Measure who actually generates alpha

Collect predictions and investment ideas from internet experts, backtest what they said in the past, and rank authors and sources by observed investing performance.

Turn public content into live investment theses

Extract ticker exposure, thesis, direction, timing, and confidence from noisy videos, posts, interviews, and threads in a structured form.

Decide whether new ideas are worth trusting

When a source publishes a new idea, score it using that source’s actual historical track record and related thesis outcomes instead of relying on raw charisma.

Built for source-driven alpha discovery

Most investing tools start with a ticker. AI Frontrunner starts with the author, source, and thesis history behind the ticker, so you can tell who consistently adds signal, who destroys it, and which live ideas deserve capital today.

The output is not another noisy feed. It is a continuously refreshed ranking of theses and tickers weighted by historical source quality and current conviction.

X / Twitter

Track recurring stock calls, macro predictions, and thesis updates from high-signal finance accounts.

Reddit

Separate reflexive hype from durable retail insight by tracking which communities and authors were actually right.

YouTube

Backfill creator history, extract market-related ticker theses from videos, and benchmark channels against forward market outcomes.

1

Collect expert sources

Register internet authors, channels, and communities that regularly publish market views and stock ideas.

2

Reconstruct historical predictions

Backfill their old content and rebuild what they were actually arguing, buying, avoiding, or shorting over time.

3

Extract theses and asset calls

Convert raw content into structured ideas with assets, thesis, direction, time window, and supporting rationale.

4

Backtest what actually worked

Compare those ideas against subsequent market data to estimate which authors and sources have historically generated alpha.

5

Rank current opportunities

Use historical trust, current thesis quality, and market context to keep a live ranked list of the most interesting assets and narratives.

Access for agent-driven financial research

The current preview is free. Paid agent access remains dormant until credentials, rights, entitlements, metering, and transport gates pass.

Bring your own agent; AI Frontrunner supplies normalized evidence, deterministic tools, provenance, and durable research records.

Organization access previews

Team access is design-partner only while organization identity, rights, quotas, usage, and workspace isolation are verified end to end.

Design Partner

Preview

A bounded platform-evaluation path for a team bringing its own financial-research agents.

Custom

Annual contract

One defined agent workflow and golden-query suite

Rights, identity, and workspace-boundary review

Compatibility and evidence-quality scorecard

No production activation before required gates pass

Join design preview

Platform Team

Planned multi-user agent access with organization-owned controls and usage accounting.

Custom

Annual contract

Organization-owned credentials and role boundaries

Shared capability, quota, and usage administration

Workspace-scoped evidence and durable receipts

Private data only after rights and isolation gates pass

Discuss team access

Enterprise

Custom deployment, controls, and commercial terms.

Custom

Annual contract

Custom agent, usage, and research limits

Security, data-rights, and procurement review

Dedicated integration and conformance support

Private data-source roadmap after isolation approval

Contact sales

Make internet investing research auditable.

AI Frontrunner is built for investors who want more than another feed of hot takes. It turns public market commentary into a ranked evidence layer with backtests, source trust, and realtime thesis monitoring.

If a source was early and right in the past, that should matter. If a source has a long history of bad calls, that should matter too. The product is built around that discipline.

Research operators

Use one evidence layer to decide which internet experts deserve analyst attention and which narratives should be ignored.

Self-directed investors

Replace scattered screenshots and notes with a ranked asset list built from trusted sources with proven historical performance.

Trading teams

Benchmark conviction by source track record and thesis quality instead of letting the loudest narrative dominate the process.

Operator Workspace

Replace opinion streams with a trust-weighted market intelligence system.

Track expert calls, score them against history, and keep a live ranked view of the tickers and theses that matter most right now.

Designed for investors who want to extract alpha from the internet without trusting every loud account equally.