Deterministic reconciliation. Verifiable audit evidence.
Settler turns fragmented transaction flows into reproducible runs, explainable exceptions, and replayable evidence. It is built for teams that need to inspect how a reconciliation result was produced.
Neutral settlement truth across systems
Settler normalizes source records, applies explicit matching policy, and preserves the evidence needed to review an outcome without depending on a single processor's view.
Normalize
Map processor, bank, commerce, and ledger records into a consistent reconciliation model.
- Explicit source provenance
- Adapter-level validation
- Stable normalized fields
Reconcile
Execute deterministic matching rules with declared tolerances and reviewable policy.
- Repeatable inputs and outputs
- Tolerance-aware decisions
- Explained mismatches
Evidence
Export run context and tamper-evident artifacts for audit support and independent verification.
- Hash-linked manifests
- Replay support
- Operator decision history
A pilot with explicit acceptance criteria
Start with one payout-to-bank-to-ledger workflow. Measure reproducibility, exception quality, evidence completeness, and operator review time against your own data.
Bound the workflow
Choose named sources, a fixed period, and a documented matching policy before execution.
- Known input population
- Declared tolerances
- Named data owners
Prove repeatability
Run the same input twice and compare outputs, hashes, and exception classifications.
- Replay the run
- Compare fingerprints
- Investigate any drift
Review the evidence
Have finance, engineering, and audit stakeholders inspect the same evidence package.
- Trace source lineage
- Review decisions
- Record acceptance gaps
How it works
From raw data ingestion to verifiable evidence, Settler ensures every step is reproducible.
Connected Ecosystem
Ingest transaction data through adapters for payment, commerce, accounting, banking, and ERP systems. Custom sources connect through the adapter framework, then normalize into a shared model for deterministic matching.
Operator-Grade Triage
Exceptions carry deterministic "why" context and operator decisions stay auditable. Where AI is enabled, it is advisory, evidence-linked, and bounded — humans keep final authority.
Explore Platform ControlsCore Architecture & Shipped Modules
Review the system surfaces behind deterministic matching, reconciliation workflows, and evidence generation.
Operational Capabilities Grounded in Code
Deterministic reconciliation engine
Rules-as-code matching with field-level tolerance controls. Same inputs always produce same outputs.
- Configurable match policies
- Tolerance-aware comparison
- Deterministic hash verification
Evidence-first output
Configured runs can produce hash-linked manifests and exportable evidence for later review.
- Structured evidence JSON
- Run provenance chains
- Export-ready audit bundles
Exception adjudication
Exceptions carry deterministic context. Operator decisions are auditable and become institutional memory.
- State-machine triage workflow
- Decision audit trail
- Policy-aware exception context
Replay and drift detection
Re-execute any historical reconciliation and compare hash outcomes to detect drift.
- Full run replay
- Hash-verified determinism
- Drift detection across executions
Integration adapters
Adapters and import paths span payment, accounting, commerce, banking, ERP, and billing systems.
- Stripe, PayPal, Square, Shopify
- QuickBooks, Xero, NetSuite, SAP
- Plaid, TrueLayer, Chargebee, +14 more
Operational controls
Role-scoped workflows, review surfaces, exports, and policy controls support governed operations.
- Approval and review workflows
- Advisory, evidence-linked automation
- Tenant-scoped operator surfaces
Start from your role
See Settler in action
Explore the operator console with realistic reconciliation data, evidence artifacts, and exception workflows. No account required.