QUORUM — AI Investment Committee
An AI investment committee that argues before it decides — and never makes up a number.
The problem
Almost every "AI invests for you" demo fails the same way: the model states a number with total confidence and the number is wrong — a made-up P/E, a misremembered drawdown, a hallucinated return. In investing, a confidently wrong number is worse than no answer.
The fix is architectural: split the system in two — deterministic computation underneath, language on top — so the failure mode is structurally impossible.
Architecture
- 1
Universe + screen
51-name, 11-sector investable universe; point-in-time shortlist of the decision-relevant names.
- 2
Research briefs
Per-name evidence assembled from the tools layer.
- 3
Bull & Bear
Research independently, argue their case, then rebut each other — genuine disagreement, not one prompt nodding along.
- 4
Macro strategist
Adds regime context.
- 5
Risk officer
Computes the downside (vol, beta, VaR, drawdown) and can veto.
- 6
Portfolio manager
Synthesizes the debate into actual weights.
- 7
Critic
Stress-tests for groupthink — then the committee loops or converges.
- 8
Memo + human gate
Allocation, rationale, and surviving dissent; streamed live to the Committee Room over SSE; a paper portfolio advances daily.
Key tradeoffs
Determinism boundary — prices (yfinance), fundamentals (SEC EDGAR), macro (FRED), and risk (NumPy/SciPy) are computed in Python; a grounding guardrail rejects any unsourced number; the LLM only narrates.
Why · This is the whole point: it kills the confident-wrong-number failure that ruins AI-investing demos.
Bull and Bear research independently before they rebut.
Why · Genuine disagreement has to be engineered — a committee that always agrees is just one opinion in six hats.
Honest backtest — point-in-time, no lookahead, trading costs included, vs SPY; reported as directional.
Why · Backtests are small-sample and regime-dependent; better to under-claim than oversell an alpha.
Runs with zero keys on free data, with provider failover on top.
Why · Robustness and $0 — a single rate limit never takes the committee down.
Eval results
$10k paper book, point-in-time (no lookahead), trading costs included — reported as directionally reasonable, never "beats the market".
Every figure computed in Python; a grounding guardrail rejects unsourced numbers before they enter the debate.
The paper portfolio is advanced by a scheduled GitHub Action, accumulating a real history.
Production proof
The artifact that keeps the numbers honest — the eval harness / monitoring gates that run in CI, not a one-off notebook result.
Honest-by-construction backtest
CI · passingResults are reported as directionally reasonable, never as an alpha claim; low-confidence decisions are flagged, not hidden.
Let's talk
I'm focused on finance AI — credit risk, RegTech, AML, and agentic investment research. Open to roles, mentorship, and collaborators in fintech, quant, and bank AI.