LIVE / FALL 2026·portfolio.status = accumulating

AI agents that own
and operate media properties.

Impetuous is an AI-native media company. Our engine finds underserved search demand, builds useful assets around it, and compounds a portfolio of properties it publishes, monitors, and refines on its own.

assets
keyword × domain × monetization × type
cadence
weekly cohorts, labeled outcomes
signal
ranking · links · revenue
horizon
portfolio, not per-page
01 / thesis

The closest analogy isn't a magazine. It's a quant trading firm whose instruments are media assets.

Enormous numbers of keywords have real demand and weak supply, but each is too small for a human content team. A page might be worth ~$20/year in display — nobody builds it.

If an agent can find, produce, publish, and measure that page for a few dollars, the economics flip. Search stops being a per-page decision and becomes a portfolio game. The long tail nobody can afford to serve is our name and our market.

We don't sell the engine. We own its output.

unit of optimization
keyword cluster
× domain authority
× monetization path
× asset type
× ranking probability
→ maximize expected value per unit of authority
02 / the loop

A closed loop, run end-to-end. Outcome data reprices what gets built next.

01
Discover

Keyword & competitor discovery. SERP-intent analysis.

02
Score

Rank each opportunity by expected value per unit of authority.

03
Plan

Asset type: table, dataset, calculator, directory, ranking, benchmark.

04
Draft

Agents produce and edit assets from primary data.

05
Evaluate

Quality gates and citation-worthiness checks before publish.

06
Publish

Ships to domains we own with internal linking and refresh cadence.

07
Measure

Ranking, backlinks, clicks, revenue — labeled cohort by cohort.

08
Learn

Outcomes feed selection. Later cohorts rank and monetize better than earlier ones.

Tools and agencies see the draft, never the P&L. We see both — and it's the outcome data, fed back into selection, that compounds.

03 / assets we build

As generic AI text loses clicks, durable value concentrates in structured, data-backed assets — the kind answer engines cite and buyers still click through to act on.

asset_type
Comparison tables

Side-by-side buyer research.

asset_type
Datasets

Primary data that earns citations and backlinks.

asset_type
Calculators

Interactive tools with buyer intent.

asset_type
Directories

Structured coverage of a category.

asset_type
Rankings

Opinionated ordering with evidence.

asset_type
Benchmarks

Reproducible measurement anchors.

04 / economics

Power-law revenue. Compounding at the engine layer.

A single site plateaus. A decision engine that sharpens with every published asset doesn't. Every property the engine operates makes the next selection decision better — marginal properties get cheaper to launch and likelier to rank.

revenue_per_asset.pyillustrative
display$20/yrinformational
affiliate$500/yrbuyer intent
lead-gen$2,000/yrhigh-value verticals
sponsorship$5,000/yrauthority properties

// optimize expected value per unit of authority, not article count

05 / verticals in scope
SoftwareFinanceTravelHome ServicesEducationLegalLocalB2B Tools
06 / operators

We've each run one half of this by hand, at real scale.

Jack Lau
CEO
Miami, FL

Grew Litespace's site 0 → 500K monthly visits. Scaled Searcle AI to ~$500K contracted ARR in 6 months. Ex-Bridgewater, IMC.

Wei Hu
CTO
Toronto, ON

Systematic trader at Susquehanna (SIG). Algo-dev intern at Hudson River Trading. Builds the agentic systems and data pipelines.