DecisionLedger AI

    AI Risk Decisioning

    Govern the risk in every AI-driven decision

    Put a risk layer over every AI-driven decision. Score algorithmic impact before deployment, verify model provenance, measure whether human oversight actually changes outcomes, and detect drift, all with explainable, auditable evidence.

    Pre-Deploy Impact ScoringExplainable Every DecisionContinuous Drift Detection

    Challenges We Solve

    Governance gaps that structured controls and audit trails eliminate.

    AI Decisions You Cannot Explain

    Models produce outputs no one can justify to a regulator or a board. Attach explainability and a scored rationale to every AI-driven decision so you can always show why.

    No Risk Score Before Deployment

    Models ship without a formal impact assessment, so risk surfaces only after harm is done. Score algorithmic impact and regulatory exposure before a model influences a decision.

    Rubber-Stamp Human Oversight

    Human-in-the-loop checkpoints exist on paper but never change outcomes. Measure whether oversight actually alters decisions, and fix the checkpoints that do not.

    Silent Model Drift

    Decision quality decays as data and behavior shift, with no alarm. Detect drift continuously and flag when a model diverges from its approved baseline.

    Use Cases

    Real governance scenarios powered by DecisionLedger.

    Chief Risk Officer

    Scores algorithmic impact and regulatory exposure before any model influences a decision, routing high-risk models for review.

    Caught model risk before deployment, not after harm

    Head of AI / ML

    Verifies model provenance and uses human-in-the-loop efficacy scoring to fix oversight checkpoints that rubber-stamp instead of changing outcomes.

    Oversight that demonstrably changes decisions

    General Counsel

    Relies on explainable, immutable records of every AI-driven decision to defend outcomes to regulators and demonstrate EU AI Act and LL144 readiness.

    Defensible AI decisions on demand

    Measurable Impact

    Based on platform benchmarks across early adopters.

    Risk Visibility

    After deploymentScored pre-deploy
    Caught early

    Explainability

    Black-box outputsScored rationale
    Always defensible

    Human Oversight

    Rubber-stampEfficacy-measured
    Oversight that works

    Drift

    Discovered in auditsDetected continuously
    Early correction
    Platform Features

    A Risk Layer Over Every AI Decision

    Score, govern, and audit the risk in AI-driven decisions, with explainability built in.

    Impact Assessment

    Formal algorithmic impact assessment with statistical bias analysis and EU AI Act article scoring before deployment.

    Oversight That Works

    Verify model provenance and measure whether human-in-the-loop checkpoints actually change outcomes versus rubber-stamping.

    Drift & Audit

    Continuous drift detection and an explainable, immutable audit trail for every AI-driven decision.

    Connects With

    Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics

    Salesforce logoSalesforce
    HubSpot logoHubSpot
    Stripe logoStripe
    Shopify logoShopify
    Google Analytics 4 logoGoogle Analytics 4
    Workday logoWorkday
    QuickBooks logoQuickBooks
    Snowflake logoSnowflake
    Slack logoSlack
    Zendesk logoZendesk
    GitHub logoGitHub
    Meta Ads logoMeta Ads
    Mailchimp logoMailchimp
    NetSuite logoNetSuite
    Jira logoJira
    Power BI logoPower BI
    Salesforce logoSalesforce
    HubSpot logoHubSpot
    Stripe logoStripe
    Shopify logoShopify
    Google Analytics 4 logoGoogle Analytics 4
    Workday logoWorkday
    QuickBooks logoQuickBooks
    Snowflake logoSnowflake
    Slack logoSlack
    Zendesk logoZendesk
    GitHub logoGitHub
    Meta Ads logoMeta Ads
    Mailchimp logoMailchimp
    NetSuite logoNetSuite
    Jira logoJira
    Power BI logoPower BI

    Featured Models

    Pre-built decision models ready to run with your data.

    Algorithmic Impact Assessment

    Formal AIA as quantitative decision model with statistical bias analysis, EU AI Act article scoring, and remediation roadmap

    Risk Matrix

    Decision Drift Detection

    Identifies when decisions diverge from original intent over time.

    multi_dimension_drift

    Regulatory Exposure Model

    Quantifies compliance risk and downside across scenarios using risk scoring per regulatory domain, Monte Carlo simulation for potential penalty exposure, and scenario modeling for best/worst/expected outcomes.

    risk_assessment_monte_carlo

    Model Provenance Attestation

    Verifies model lineage, training data attestation, and supply-chain trust.

    Risk Matrix

    Human-in-the-Loop Efficacy

    Measures whether HITL checkpoints actually change outcomes versus rubber-stamping.

    Bayesian Inference

    Multi-Agent Check Bypass Detection

    Detects when multiple AI agents converge on outcomes that bypass intended controls.

    Anomaly Detection

    How It Works

    Three steps to structured, auditable decisions.

    1

    Assess Before Deploy

    Run a formal algorithmic impact assessment and regulatory exposure score before any model influences a decision. High-risk models route for review.

    2

    Govern at Decision Time

    Verify model provenance, enforce human-in-the-loop where it matters, and detect when multiple agents converge to bypass intended controls.

    3

    Monitor & Prove

    Detect drift continuously and record an explainable, immutable trail of every AI-driven decision for regulators, auditors, and the board.

    Replace Your Stack

    When an AI-driven decision is challenged, can you score its risk, explain its rationale, and prove a human meaningfully reviewed it, or only after the fact?

    ×

    Spreadsheet model risk logs

    Manual, stale records that miss real-time drift and bypass

    ×

    Generic MLOps monitoring

    Watches accuracy, not decision risk, explainability, or oversight efficacy

    ×

    After-the-fact AI audits

    Risk surfaced only once a model has already caused harm

    ×

    Point AI-governance checklists

    Static attestations with no enforcement at decision time

    All in one governed platform

    Start with AI Risk Decisioning today

    See how DecisionLedger AI transforms your decision-making.