We build what AI makes possible.

Government debt is rising faster than economic output across much of the developed world. History shows that an escalating sovereign-debt pathway can become a systemic economic and geopolitical threat. The critical question is not simply how much debt governments issue, but whether investor demand will continue to absorb it on acceptable terms.

Rather than relying on delayed commentary or secondary-market interpretation, this platform monitors the primary market itself—where governments actually raise capital. It collects, validates, and analyzes official G7 auction results to assess the health of sovereign-debt demand directly from the issuers in near real time.

The system combines official-source acquisition, parsing, deterministic validation, historical normalization, publication logic, desktop operation, scheduling, and email delivery. It represents an end-to-end implementation of governed agentic software engineering: reasoning models directed architecture while coding agents executed bounded tasks.

A modular quantitative desktop application that scans macro volatility, positioning extremes, and rates metrics across mega-liquid futures markets (/SR3, /ZB, /ZN, /ES) to detect cyclical volatility compression floors and option convexity.

Built end-to-end under a dual-agent governed spec-driven development (SDD) workflow, the platform integrates multi-source public ingestion, rolling multi-year percentile and z-score mathematics, Black-76 vectorized option pricing with Brent’s root-finding, an automated 4,996-day SQLite historical backfill, and an asynchronous multi-panel Tkinter visualization engine.

A high-precision retrieval-augmented generation system that turns large contract records—agreements, amendments, correspondence, and obligations—into source-grounded analysis for negotiation preparation and dispute resolution.

Commercial negotiations and disputes are usually decided by what the documentary record actually says. This system makes that record fully usable: every conclusion is tied to specific contract language and supporting evidence, so positions are defensible, auditable, and ready for decision-makers and counsel.

A 6,054-line interactive analytical platform engineered to extract forward-predictive signal from a heterogeneous physical system spanning ~9,000 square miles, resolving the non-linear tugs and pulls of 29+ interacting variables directly from empirical observation.

Rather than trusting a single algorithm with a monolithic inductive bias, the architecture deploys a hierarchical stacked ensemble. Seven distinct model families—gradient-boosted trees, random and extra-trees forests, histogram boosting, neural networks, and linear baselines—are trained simultaneously to capture complementary response structures. Their out-of-fold predictions feed into a tunable meta-learner that weights base models dynamically and cancels out uncorrelated error. Coupled with slider-driven hyperparameter interrogation and asynchronous retraining workers, the platform gives practitioners direct, tactile visibility into bias–variance trade-offs, response-surface topology, and base-model error complementarity before final assembly.

A desktop retrieval-augmented generation system that turns disconnected technical documents into searchable, source-grounded knowledge for analysis and decision support.