Solving Institutional Epistemological Crisis in Credit Risk
ADAM (v30.1) is an institutional-grade, local-first neuro-symbolic framework engineered specifically to bridge stochastic language models with deterministic financial mathematics. Designed for high-stakes environments—Broadly Syndicated Loans (BSL), Distressed Debt, and Deep Credit Underwriting—ADAM enforces strict separation between stochastic semantic parsing and deterministic calculation via a Probabilistic-to-Deterministic Integration Layer (PDIL).
FinanceBench Accuracy
99.0%
Deterministic verification vs LLM text
Rust Engine Latency
< 5 ms
Zero GIL lock for Monte Carlo & DCF
EDGAR Ingestion Speed
< 12 sec
10-K parsing to FIBO graph update
Deterministic Drift
0.0%
jsonLogic verified proof-of-thought
The "System 2" Cognitive Triad Architecture
Explore the three complementary computational systems that form ADAM's sovereign intelligence layer.
System 1Asynchronous Hive
The Neural Swarm (Reflexes)
High-velocity, unstructured data parsing and continuous surveillance. Ingests 13F, 13D, and Form 4 SEC EDGAR filings to capture qualitative market sentiment and structural shifts.
✓ High-speed NLP extraction
✓ Sentiment & signal vectorization
✓ Unstructured EDGAR parsing
System 2Neuro-Symbolic DAG
The Graph (Deep Thinker)
"Logic as Data" policy enforcement using a Graph Neural Network (GNN) and Financial Industry Business Ontology (FIBO). Evaluates target companies and tests covenant compliance.
✓ FIBO Knowledge Graph GNN
✓ Capital structure & covenant checks
✓ Deep credit memo generation
System 3Quantum Simulation
World Model & Quantum
Models asset price trajectories and systemic risks via jump-diffusion frameworks and Qiskit/cuQuantum Quantum Amplitude Estimation (QAE) for extreme tail-risk.
To ensure absolute auditability and zero hallucination, ADAM executes credit risk underwriting through a strict 5-stage sequential pipeline. Raw unstructured data is ingested by System 1, routed through the Probabilistic-to-Deterministic Integration Layer (PDIL), structured via FIBO GNN knowledge graphs, and passed to compiled Rust engines for mathematical execution.
Compiled Rust execution of core financial math (PD, LGD, VaR, Monte Carlo) in < 5ms.
Live PDIL Execution Engine
Interactive Credit Risk & Covenant Stress-Tester
Experience ADAM's deterministic pricing and covenant testing engine in real-time. Adjust capital structure parameters, EBITDA stress shocks, and loss given default (LGD) assumptions below. The system instantaneously calculates Expected Loss (EL), Value-at-Risk (VaR 99%), Interest Coverage, and Covenant Headroom—completely bypassing stochastic LLM math.
Underwriting Parameters
$120.0M
$380.0M
$120.0M
-15.0%
45.0%
Implied PD2.84%BB+ Sovereign
Expected Loss (EL)$6.39MPD × LGD × EAD
Stressed Leverage4.90x+0.10x Headroom
Covenant StatusCOMPLIANTPassed Rust Rule Engine
Stress Test Expected Loss Sensitivity
Expected Loss ($M) across EBITDA shock severity curves
Deterministic Engine
Capital Structure & Debt Waterfall
Breakdown of Senior Debt, Subordinated Debt, and Asset Coverage
Specialized Agent Matrix
Autonomous Agent Swarm Directory
ADAM operates a decentralized hive mind of highly specialized autonomous agents located in core/agents/. Each agent operates under strict capability boundaries, using model context protocols (MCP) to query data, execute mathematical tools, and pass verifiable telemetry to the central orchestrator.
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Performance & Strategy
Institutional Benchmarks & Autonomy Roadmap
ADAM is continuously benchmarked against industry standards like FinanceBench to verify calculation integrity and zero-hallucination execution. Below are empirical comparison charts, technical directory maps, and the phased roadmap towards total institutional autonomy.
FinanceBench Accuracy Comparison (%)
Deterministic calculation verification vs standard stochastic LLMs
Execution Latency Comparison (ms)
Compiled Rust Primitives vs Python & LLM Inference