Financial Risk Control
Transaction monitoring and anomaly review
Explainable AI for high-stakes decisions
Decision Automation Built on Clear Logic, Not Black Boxes
logicstact helps teams in financial risk control, legal compliance, and supply chain optimization deploy explainable AI decision engines that combine transparent reasoning with a robust rule engine.
Transparent reasoning traces
Hybrid logic + rule orchestration
Audit-ready decision evidence
Legal Compliance
Clause analysis and policy alignment
Supply Chain Optimization
Warehouse and routing decision operations
Built for Regulated and Operationally Complex Industries
We combine machine reasoning and explicit rules so your teams can automate decisions without losing control or explainability.
Financial Risk Control
Fraud detection, credit policy checks, and adverse-event reasoning with full decision traceability.
Legal Compliance
Policy-to-rule translation, regulatory alignment checks, and evidence-backed recommendations.
Supply Chain Optimization
Exception triage, routing decisions, and service-level trade-off decisions with transparent rationale.
Teams piloting our decision engine
North River Bank
LexBridge Counsel
TransitAxis Logistics
Meridian Finance Group
Frontline Procurement
Atlas Compliance Partners
Product Details
A modular platform for explainable decision automation, built to run safely in regulated environments.
Explainability Engine
Reasoning Core
Build multi-step inference flows that stay transparent under scrutiny.
- Evidence-linked reasoning graph for each decision
- Counterfactual simulation for what-if analysis
- Confidence, contradiction, and fallback handling
Hybrid Rule Engine
Rule Fabric
Compose versioned policy rules and blend them with AI-generated insights.
- Business, legal, and risk rules in one policy layer
- Deterministic overrides for high-risk scenarios
- Version control and approval workflow for rule updates
Production Orchestration
Decision Ops
Deploy decisions in real time with governance, review, and observability.
- Real-time API with latency-aware decision serving
- Human-in-the-loop escalation and approval queues
- Audit log export for regulators and internal controls
Platform Architecture
Designed for enterprise integration, high-stakes policy control, and production-grade decision delivery.
Input & Integration Layer
- REST / event-stream ingestion
- ERP, CRM, case management connectors
- Schema validation and data quality guards
Reasoning + Rules Layer
- Multi-step reasoning graph execution
- Policy and legal rule arbitration
- Conflict resolution and deterministic overrides
Decision Delivery Layer
- Real-time decision APIs
- Human review queues and escalation paths
- Audit export and governance observability
How Decisions Are Produced
A transparent decision pipeline that combines machine reasoning with deterministic controls.
1
Ingest Contextual Signals
Collect structured and unstructured inputs such as transactions, contract clauses, exception logs, and policy metadata.
2
Build Explainable Reasoning Path
Generate a step-by-step inference graph that captures assumptions, evidence weights, and contradiction checks.
3
Apply Deterministic Rule Arbitration
Run legal, risk, and business rules to override, constrain, or validate AI inferences before execution.
4
Deliver Decision + Audit Evidence
Return the action, confidence profile, reason codes, and a complete trace for auditors and operators.
Decision Scenarios in Production
See how the engine handles high-stakes decisions with traceable logic from trigger to final action.
Financial Risk Control
Real-Time Payment Risk
Trigger: Cross-border transfer exceeds dynamic anomaly threshold.
Reasoning: Combines account behavior drift, counterparty risk profile, and temporal transaction burst patterns.
Action: Auto-hold transfer and escalate to analyst with pre-filled evidence bundle.
Legal Compliance
Contract Clause Compliance
Trigger: New vendor contract includes non-standard indemnity and jurisdiction clauses.
Reasoning: Maps clause semantics to policy obligations and runs contradiction checks against approved templates.
Action: Route for legal review with clause-level risk labels and remediation suggestions.
Supply Chain Optimization
Disruption-Aware Route Decision
Trigger: Weather disruption causes SLA breach risk for regional deliveries.
Reasoning: Evaluates reroute options using lead-time impact, penalty exposure, and warehouse capacity constraints.
Action: Automatically switch to best-fit route plan and notify operations with rationale.
Governance and Control Layer
Built for teams that need automation speed without compromising oversight, accountability, or policy compliance.
Policy Version Governance
Track every rule change with approvals, rollback points, and deployment history.
Human-in-the-Loop Escalation
Auto-route edge cases and high-impact outcomes to reviewers with contextual evidence attached.
Model + Rule Drift Alerts
Detect shifts in input distributions, policy conflicts, and decision behavior over time.
Audit Package Export
Generate complete decision records for internal control, model risk governance, and external review.
Decision Coverage Matrix
A clear map from business scenario to automated action and explainable evidence output.
| Scenario | Domain | Automated Decision | Explainable Evidence |
|---|---|---|---|
| Fraud & Payment Review | Financial Risk Control | Approve, Hold, or Escalate transaction | Reason code chain + policy checks + anomaly context |
| Regulatory Clause Assessment | Legal Compliance | Accept, Revise, or Reject contract clause | Clause mapping + conflict trace + jurisdiction flags |
| Supply Disruption Response | Supply Chain Optimization | Reroute, Re-allocate, or Expedite shipment | Constraint analysis + SLA risk + cost trade-off rationale |
| Credit Limit Adjustment | Financial Operations | Increase, Maintain, or Reduce credit exposure | Risk inference graph + override rule history |
Customer Case Studies
Practical deployments where explainable AI improved speed, trust, and operational consistency.
North River Bank
Financial Risk ControlChallenge: Fraud rules generated high false positives and delayed analyst response during peak transaction windows.
Approach: Deployed hybrid reasoning policies that combine transaction context, behavioral signals, and deterministic risk rules.
Outcome: 37% fewer false alerts and 28% faster case resolution.
LexBridge Counsel
Legal ComplianceChallenge: Legal operations teams struggled to justify compliance decisions across multiple jurisdictions and policy versions.
Approach: Implemented policy-linked reasoning traces with clause-level evidence and versioned legal rule sets.
Outcome: 63% faster compliance review cycles with full audit evidence export.
TransitAxis Logistics
Supply Chain OptimizationChallenge: Exception handling decisions for route disruptions were inconsistent between regions and difficult to validate.
Approach: Built a decision workflow that scores alternatives and enforces SLA and contractual constraints.
Outcome: 19% improvement in on-time delivery for disrupted lanes.
Meridian Finance Group
Credit & CollectionsChallenge: Credit limit and collections decisions lacked explainability, increasing regulatory and operational risk.
Approach: Connected explainable inference models with approval thresholds and escalation logic for edge cases.
Outcome: 41% reduction in manual review workload with 100% decision trace coverage.
Measurable Results from Explainable Automation
Lower False Alerts in Risk Operations
Faster Compliance Review Cycles
Typical Decision API Latency
Decision Trace Coverage for Audits
Frequently Asked Questions
Common questions from teams evaluating explainable decision automation.