The MosaicIQ Platform
Technology that augments judgment
Proprietary, patent-pending, AI-native research software, built and maintained in-house. The platform structures our coverage universe according to a diverse, custom-built sector taxonomy tailored to the fund’s research framework. Every analytical output is timestamped and preserved — each decision reconcilable against the information available at the moment it was made.
End-to-end
One platform, three core workflows
1. Research & selection
Every covered company gets a living thesis — bull, base, and bear scenarios with testable catalysts and risks. Valuation adapts per name, a dual conviction read disciplines the universe, and a thematic layer maps hypotheses to beneficiaries and constrained names.
2. Entry, sizing & construction
Daily signals flag entry readiness, timed by a technical overlay that never overrides fundamentals. Exposure analytics catch hidden concentration, factor, and theme risk. Sizing follows conviction-weighted recommendations and a decision scorecard for each add, trim, or exit.
3. Performance measurement
Because every thesis, score, and decision is timestamped, the platform grades itself: theses are measured against outcomes, automated backtests recalibrate scoring weights toward what predicts returns, and a behavioral-bias layer audits the fund’s own decisions.
What makes it different
Discipline, compounded
Living investment thesis
Theses are versioned data models, not static documents; catalysts and risks carry status, evidence weight, and a full transition history, so a thesis evolves with the facts rather than going stale.
Evidence-to-estimate bridge
When new research shifts a tracked catalyst or risk, the platform derives a calibrated, audit-trailed estimate adjustment — connecting qualitative evidence to quantitative forecasts without losing the record.
Closed validation loop
Price targets, scenario probabilities, and implied upside are measured against what actually happened, and the lessons feed back into the scoring weights — the model is built to learn from its own mistakes.
Behavioral-bias audit
The fund’s own conviction, estimate, and position trajectories are checked against the CFA Institute bias framework, surfacing anchoring, overconfidence, and herd behavior in its own decisions.
On the roadmap
Toward multi-agent reasoning
The platform does not make decisions. It automates the observation, measurement, and bookkeeping no single analyst can sustain across the full coverage universe — freeing capacity for the high-judgment work that drives returns.