ADS.SIM // Adversarial Deal Simulator

Deterministic engine that "unit tests" credit agreements. Identify structural loopholes used to strip assets and dilute collateral with evidence-linked risk projections and auditable analysis.

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Project Overview

ADS.SIM (Adversarial Deal Simulator) is a deterministic logic engine that treats legal documentation as “legal code.” It identifies structural vulnerabilities in credit agreements by analyzing how specific clauses interact to create bugs or loopholes—such as asset leakage, lender priming, or collateral dilution.

1. Key Features

  • Adversarial Pattern Matching
    The engine scans for known structural exploits used in high-stakes debt restructurings, including:

    • J.Crew Trap Doors
    • Serta-style Uptiering
    • Chewy Asset Stripping
    • Incora Double-Dips

  • Adversarial Projections
    For every identified risk, the system generates a step-by-step “Attack Narrative,” simulating exactly how a sophisticated borrower could exploit the specific language found in the agreement.

  • Evidence-Linked Auditing
    To prevent AI hallucinations, every risk card is strictly linked to verbatim text within the document. Users can see the “Adversarial Highlight”—the exact 3–5 words that create the vulnerability.

  • Risk Vector Quantization
    Risks are categorized by severity and measured across three dimensions:

    • Recovery Risk
    • Control Risk
    • Timing Risk

  • Suggested Patch (Remediation)
    The system provides specific redline language designed to close the identified loophole while maintaining standard market flexibility.

  • Deterministic Reasoning
    Unlike standard LLM chat interfaces, ADS.SIM uses structured JSON outputs to enforce a strict legal-logic framework.

2. Target Users

ADS.SIM is designed for institutional professionals in regulated environments:

  • Credit Committees & Risk Officers
    Identify structural weaknesses in new deals before they are signed.

  • Distressed Debt & Secondary Traders
    Conduct rapid due diligence on “tightness” when buying or selling existing loan positions.

  • General Counsels & Legal Teams
    “Unit test” their own documentation against known adversarial patterns used by aggressive sponsors.

  • Portfolio Managers
    Monitor existing credits for hidden vulnerabilities as market conditions shift.

3. Tech Stack Used

Frontend

  • Built with React 19 and Tailwind CSS
  • UI uses a Terminal-to-SaaS hybrid aesthetic
  • JetBrains Mono for code-centric elements to reflect institutional precision

Dual-Model Intelligence Engine

  • Gemini 3 Flash
    Used for high-volume verbatim extraction and indexing of 500+ page documents. Focuses on ground truth retrieval of covenants, definitions, and baskets.

  • Gemini 3 Pro
    Used for deep adversarial reasoning. Leverages a long context window and high thinking budget to solve cross-clause logic puzzles (e.g., how a definition in Article I interacts with a permission in Article VI).

Structured Outputs

  • Enforced via the Gemini API’s responseSchema
  • Ensures every analysis is auditable, repeatable, and formatted for integration into enterprise risk systems

Deterministic Prompting

  • Constrained to identify only what is explicitly allowed or failed to be restricted by the text
  • Avoids speculative or predictive claims

4. System Limitations (The “Safeguards”)

  • No Legal Advice
    Identifies logic patterns, not legal conclusions.

  • No Speculation
    Does not predict borrower behavior; only analyzes structural permission.

  • Non-Predictive
    An audit-support tool, not a financial forecasting engine.

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