TRACKED CALLS / 24H1,284+12.4%SMS RECOVERIES / WK287+8.1%DEMAND REACTIVATION RESPONSE RATE41.0%+3.2ppAU + NZ CLIENTS LIVE512+11AVG QUOTE RESPONSE47 MIN-9 minISO 27001CERTIFIEDTRACKED CALLS / 24H1,284+12.4%SMS RECOVERIES / WK287+8.1%DEMAND REACTIVATION RESPONSE RATE41.0%+3.2ppAU + NZ CLIENTS LIVE512+11AVG QUOTE RESPONSE47 MIN-9 minISO 27001CERTIFIED
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Sandbox · We run this ourselves. Not as a sales pitch, as the day job

// Internal lab · multi-model AI experiment · not for sale

Investing AI Terminal

Internal lab · multi-model AI experiment · not for sale

Investing AI Terminal

LAB

// experiment notebook · not for sale · published as-is

Hypothesis

If you wire an Australian retail portfolio (AUD-denominated, momentum + dividend + venture buckets) to a news + VIP + geo signal pipeline scored by multiple LLMs, does the AI-led signal beat the SPY benchmark over a multi-month live run?

Current state (honest)

Down 38.4% vs SPY since inception. The terminal is honest about this. Most companies would hide a losing run. We publish it because the point of the lab is not to make money in retail equities, it is to stress-test how different AI models score news signals against a real ledger.

Investing AI terminal showing portfolio AUD 14,054 +0.39 percent vs cost, P&L vs SPY at minus 38.4 percent, 10 positions, momentum dividend and venture bucket allocation, signal history density chart.
The Investing AI terminal. Down minus 38.4 percent vs SPY since inception. Published as-is because the point is the experiment, not the win.

What we are testing

  • 01Claude Sonnet for news signal scoring
  • 02Claude Opus for cross-signal reconciliation (news + VIP + geo)
  • 03Claude Haiku for fast headline screening
  • 04Gemini for sentiment delta on AU vs US news
  • 05Open-model baselines from Hugging Face for comparison
  • 06Manual VIP-signal weighting (specific operators we trust to call inflection points)
  • 07Geo-signal correlation (regional indicators against AUD/USD)
  • 08Editorial operator UI as a forcing function for signal explainability

What it has taught us (transfers to client work)

Three things we have learned that DO transfer back to the day job. One: AI scoring of news is overconfident if you do not penalise it for novelty. Two: multi-model consensus catches errors single models miss, which is why every Gibson production system runs two-model review. Three: the discipline of publishing the ledger forces honesty. We apply that same discipline to client reporting now.

Running since April 2026. Internal only. Not productised. Not for sale.

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