End-to-end AI solutions, driving results in production.
Private credit lenders and PE credit investors had no independent, real-time reference for consensus loan pricing across BDC portfolios. Valuation divergences — like one lender marking a borrower at 28.7¢ while another marked it at 99.9¢ — were invisible until it was too late.
GIA built GI Bletchley, a production AI surveillance platform that ingests structured EDGAR filings across 150 BDCs, normalises borrower-level data, and computes the Bletchley Consensus Mark — a weighted consensus price across all reporting lenders for each borrower. The SIMONS pattern engine flags divergences and deterioration signals in real time.
Private credit origination is labour-intensive. Sourcing targets, gathering financials, running credit analysis, and producing investment committee memos each require specialist time that does not scale.
GIA built VALIS — an AI-native origination platform that identifies target borrowers, enriches data from Companies House and SEC EDGAR, scores credit quality, and generates full IC memos automatically. The system covers UK and US markets and integrates outreach automation for high-volume prospecting.
Systematic trading strategies degrade as market regimes shift. Manual strategy review is slow and biased. Backtesting infrastructure is fragmented across data sources and execution environments.
GIA built ATLAS — a 24-agent AI trading swarm deployed on Azure. The SIMONS pattern engine identifies 31 confirmed mean-reversion patterns. The JANUS meta-layer detects regime shifts and reweights the agent cohort dynamically. Darwin v3 uses evolutionary optimisation to continuously improve agent prompts without human intervention.
Early warning signals in private credit portfolios are buried in unstructured BDC filings. No systematic approach existed for pattern detection at scale across the market.
GIA built the SIMONS-CREDIT pattern engine — trained on 1,159 clean loans across 22 sectors from SEC EDGAR BDC filings. 35 patterns validated. The engine identifies divergence signals between lenders marking the same borrower at materially different prices, flagging CRITICAL status before the market reprices.
A 70,000-word political memoir required substantive editorial development across 11 specialist functions — structural analysis, fact-checking, tone calibration, narrative coherence, political sensitivity review. Traditional editorial processes take months and cost six figures.
GIA built an 11-agent AI editorial pipeline — each agent a specialist running sequentially and in parallel across the manuscript. Agents cover structural editing, fact-checking, tone analysis, political sensitivity, pacing, and consistency. The human author retains full creative control; agents handle the analytical load.