The #1 agent for customer service.

Eturns learns how you decide, then takes those decisions on your behalf — returns, exchanges, refunds, warranty, tickets, tracking — within the policies you set, with a full reasoning trace on every case. Solutions at the speed of thought.

Install

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AI operator works the way your best self would — if you had unlimited time.

Proposes & executes For every case, Eturns generates a resolution plan, scores it against your policy, and executes the safe ones automatically.

Policy-bounded Confidence thresholds, dollar limits, and fraud gates keep autonomy inside the rules you wrote. Risky cases escalate to you.

Full audit trail Every decision logs the model used, the prompt, the retrieved context, the tool calls, and the reasoning trace.

One decision layer across every after-sales module

Returns, exchanges, warranty, tracking, reviews, tickets, fraud, and recovery offers share the same agent loop. The agent handling a return benefits from knowing the customer’s ticket history. The agent handling warranty benefits from knowing the recent fraud signals. Cross-module integration is not a feature bolted on — it is the architectural foundation that point-solutions cannot easily build because they are separate companies with separate data models.

How the agent decides

Constitution An immutable, versioned snapshot of your policies is bound to each case. In-flight cases never silently change meaning when you edit a setting.

Candidates For every issue, Eturns proposes zero or more remedies — refund, exchange, store credit, escalate — each with estimated customer, margin, and risk effects.

Scorecard Each candidate is scored against policy, evidence, risk, margin, confidence, and executable-action gates. Disqualified candidates are kept for audit.

Judgment One candidate is selected or the case is escalated to human review. Confidence, explanation, and model provenance are persisted.

Fulfillment Executable judgments run as an idempotent dependency graph — refund, label, exchange, store credit — each reconciled against the real-world result.

Outcome The observed result is fed back into Learning, so the next case is calibrated against what actually happened, not what was predicted.