Fundus runs specialist AI agents against each other, passes every thesis through independent risk gates, and hands you a structured decision record to sign — or reject — before any capital moves.
Cognitive Consensus Graph
Fiduciary-grade infrastructure
01The problem
02The solution
Fundus replaces generic AI chat and fragmented research with a stateful, graph-orchestrated consensus engine—moving from signal to decision through typed artifacts, contextual memory, and algorithmic risk control.
02Capabilities
Each module enforces a rigorous computational boundary—from strict point-in-time data ingestion to human-approved execution intent.
Ingest and normalize multi-provider feeds with strict temporal isolation (as_of), so no future data leaks into the analysis.
Parallelize signal extraction across isolated domain experts to prevent cross-contamination and single-model confirmation bias.
Subject every thesis to bounded bull-vs-bear scrutiny. Retrieve historical contextual memory to stress-test rationale before approval.
Model regime-aware volatility and enforce drawdown ladders. An independent risk layer sizes, blocks, or flags proposals before human approval.
Orchestrate cross-asset sleeve weights, evaluate algorithmic hedge overlays, and trigger automated defensive protocols during market shocks.
Trace a deterministic audit trail from signal to execution intent, requiring explicit thesis-invalidation criteria and human signature prior to capital deployment.
03Who It Serves
Fundus fits the operational footprint of teams that demand deterministic structure, not stochastic AI shortcuts.
Replace a fragmented patchwork of terminals with a unified inference pipeline—from initial thesis to risk-constrained portfolio action.
Execute bounded adversarial debates and regime-aware sizing within a single deterministic trace instead of scattered trading platforms.
Review dynamic sleeve weights, algorithmic hedge overlays, and automated downside protection protocols across multi-asset portfolios.
Audit a strictly typed decision ledger—analyst reports, consensus logic, and risk deliberations—prior to capital deployment.
04Pipeline topology
Explore each module in depth. Trace the exact computational path—from isolated data ingestion to human-approved capital deployment.
Strict point-in-time normalization across price, macro, and sentiment feeds. Eliminates forward-leakage before downstream routing.
Parallelized signal extraction across isolated domain experts. Prevents cross-contamination and single-model confirmation bias.
Isolated theses are subjected to bounded bull-vs-bear debates, forcing rigorous adversarial scrutiny to stress-test the rationale.
An independent, multi-posture risk layer models regime-aware volatility, enforcing strict drawdown ladders and adaptive stop logic.
Portfolio-level orchestration across asset sleeves. Evaluates defensive stances and triggers automated downside protection routing.
An append-only, replayable audit trail of analysis, consensus and risk. Nothing deploys capital without an explicit human approval.
05Technical due diligence
Need to audit the computational graph in context? Request private access for an engineering walkthrough.
06Core tenets
Fundus was engineered to solve a singular institutional failure: the gap between stochastic AI experiments and fiduciary execution. We build the computational infrastructure that connects multi-agent inference, quantitative risk, and portfolio orchestration into one auditable process.
Our architecture prioritizes mathematical rigor over generative hype, deterministic explainability over opacity, and fiduciary judgment over unconstrained automation. Every module enforces strict boundaries—designed to arm systematic strategists, not replace them.
Signal frameworks and risk models are calibrated per asset class instead of being reused as one-size-fits-all shortcuts.
Every execution intent traces back to its evidence, the opposing case and explicit thesis-invalidation criteria.
Logic gates, strict capital envelopes and an append-only ledger keep the portfolio manager in charge before any deployment.
Signal frameworks and risk models are calibrated per asset class instead of being reused as one-size-fits-all shortcuts.
Every execution intent traces back to its evidence, the opposing case and explicit thesis-invalidation criteria.
Logic gates, strict capital envelopes and an append-only ledger keep the portfolio manager in charge before any deployment.
Who is behind Fundus
Founder · Mexico City
Javier has spent more than 15 years building SCADA and control systems for critical infrastructure — rail and industrial automation — across Europe, the Middle East, Africa and Latin America. Fundus applies the same engineering discipline to investment decisions: independent interlocks, explicit execution boundaries and a human signature before anything moves.
This is a private alpha. We exclusively onboard systematic teams who require strict execution boundaries and deterministic infrastructure.
