LABARNAAutonomous Dispute Resolution EngineADRE · Autonomous Dispute Resolution Engine
Dispute resolution remains one of the highest-volume, highest-cost, and highest-risk processes in payments. ADRE brings intelligent automation to this domain with strict graduated autonomy and continuous learning from operational outcomes.
Evidence assembly, pattern-informed strategy, multi-mode filing, and closed-loop outcome feedback — ADRE handles disputes at production scale while preserving the visibility, auditability, and control that high-stakes commercial operations require.
Even with existing automation tools, most organizations still rely heavily on manual effort for evidence gathering, response drafting, and filing decisions. This creates high operational costs, inconsistent outcomes, and slow resolution times — especially as transaction volumes grow and agent-driven commerce increases the number of counterparties and transaction types.
At the same time, simply increasing automation without proper controls introduces significant risk. High-value disputes, complex fact patterns, and regulatory requirements demand oversight. Organizations need a way to safely increase automation while maintaining visibility, auditability, and control.
Existing solutions are largely rule-based or template-driven. They lack the ability to learn from outcomes across a federation of organizations and cannot intelligently balance automation with governance. A new approach is required — one that combines intelligent recommendations, evidence-driven drafting, and graduated autonomy in a single system.
Relevant evidence is gathered automatically from multiple sources. A completeness assessment determines whether a case has sufficient support for automated drafting or whether it should be escalated for human review. Every draft is persisted with provenance back to the evidence that informed it.
Strategy recommendations are sourced from SLPI's federated learning layer, incorporating relevant precedents from across the federation while preserving each organization's confidentiality. Every recommendation arrives with a calibrated confidence score and traceability to the patterns that informed it.
A three-mode filing architecture lets organizations control the level of automation by case characteristics and risk posture. Start with full visibility in shadow mode, progress through supervised review, and unlock autonomous execution only when multiple independent safety conditions are satisfied.
ADRE follows a structured, auditable process. Six stages from intake through outcome feedback, with explicit gates at every transition that produces external action — nothing leaves the building without satisfying the conditions of the mode under which the case is operating.
Evidence assembly, pattern-informed strategy, multi-mode filing, and closed-loop outcome feedback — ADRE handles disputes at production scale while preserving the visibility, auditability, and control that high-stakes commercial operations require.
ADRESTAGE 01 — DISPUTE INTAKEDisputes are received from the underlying payment infrastructure (including disputes arising from agent-to-agent transactions) as well as from external payment platforms. Incoming data is normalized into a structured case record.
Single canonical representation regardless of source. Downstream stages never have to handle format variance.Gathers relevant evidence automatically from heterogeneous data sources and evaluates completeness before any draft is composed.
Recommendations sourced from SLPI's federated learning layer, each with calibrated confidence and traceability to the patterns that informed it.
Three operational modes (shadow, supervised, autonomous) let organizations control automation level by case characteristics and risk posture.
Multiple independent conditions must be satisfied before any autonomous-mode submission. Confidence, dollar limits, case type, and policy gates all evaluated.
Every outcome feeds back into SLPI to strengthen or weaken the patterns that informed the original recommendation. Each dispute improves the next.
Simulation, supervised review, and autonomous execution stay isolated. No cross-contamination of outcomes or signals between modes.
Plugs into REAP for dispute origination and outcome enforcement, and into card networks and payment processor dispute systems for direct filing. End-to-end coverage from intake to resolution — no separate orchestration layer required.
Existing automated dispute tools rely on static rules or simple templates. Manual or lightly automated processes don't scale efficiently as transaction volumes and counterparty counts grow. ADRE addresses both limitations in one system.
×Static rules and templates that don't improve over time
×No learning from outcomes across organizations
×Automation increases either go all-in or stay manual
×Cost grows linearly with dispute volume
×Inconsistent outcomes across reviewers and cases
×Limited integration with card network filing systems
✓Pattern-informed strategy that improves with every outcome
✓Federation-wide learning via SLPI without data sharing
✓Graduated autonomy: shadow, supervised, autonomous
✓Operational cost decouples from dispute volume
✓Consistent outcomes via structured drafting with provenance
✓Direct integration with card networks and processors
ADRE is designed to operate as the domain-specific decision layer within a coordinated three-layer autonomous payment operations system. Each layer is independently valuable. Together, they create a closed-loop system that cannot be achieved by any single layer in isolation.
The foundational payment infrastructure layer. A 10-step policy-governed authorization pipeline, conditional escrow with a 5-state state machine, automated daily reconciliation, and dispute origination — the system that moves money safely between autonomous agents and surfaces disputes when they arise.
01The federated learning and decision intelligence layer. Accumulates operational experience across the federation, delivers pattern-informed recommendations with calibrated confidence, and gets smarter with every outcome — without exposing any participant's data. ADRE's strategy recommendations come from here.
02The domain-specific autonomous decision layer. Consumes recommendations from SLPI, assembles evidence, drafts structured responses, and routes filings through graduated autonomy modes (shadow, supervised, autonomous) — closing the loop back into SLPI with every outcome.
03ADRETHREE-LAYER STACKGet In Touch