Marketplace Claims
Resolution
Evidence-driven claims adjudication engine for e-commerce trust & safety teams.
Analyzes claims, chat conversations, order data, and visual evidence to determine whether a dispute is valid, fraudulent, or inconclusive.
Evidence-first. Zero guessing.
Every decision is grounded in observable signals across multiple data sources.
Multi-Source Analysis
Cross-references claim text, order metadata, product details, and visual evidence to build a complete picture before any decision.
Visual Evidence Inspection
Identifies objects, detects damage, validates packaging integrity, and flags manipulation signals like cropping or image reuse.
Fraud Pattern Detection
Scores fraud risk using signals like repeated claims, inconsistent damage patterns, category mismatches, and abnormal return history.
Structured Decision Output
Returns strict JSON with claim status, confidence, severity, fraud assessment, and evidence-grounded reasoning — ready for downstream automation.
Handles every dispute type
Category-aware validation rules for the most common marketplace claims.
Damage Claims
- Broken screens
- Cracked items
- Liquid damage
- Package damage
Wrong Item
- Incorrect product
- Different brand/model
- Mismatched specs
Missing Items
- Missing accessories
- Partial delivery
- Empty package
Food Delivery
- Missing items
- Spoiled food
- Incorrect order
Return Disputes
- Condition mismatches
- Used vs new
- Fake replacements
Built for scale
Designed to process thousands of claims per minute with consistent accuracy.
Category-Aware Rules
Electronics, fashion, food, home — each category gets specialized validation logic
Evidence Discipline
Never hallucinates image content; prefers 'insufficient evidence' over guessing
Hard Constraints
No assumptions without visual proof; conservative decisions in uncertain cases
Structured Input
Accepts user history, fraud flags, return rates, and order item lists
High-Scale Design
Built for thousands of claims per minute with consistent, repeatable outputs
Neutral Adjudication
Acts as evidence-driven claims engine, not a customer support agent
What the system does
For every claim, the engine executes a strict sequence — extract, inspect, decide, justify.
Extract damage claim
Parse the conversation to isolate the actual damage claim from surrounding context.
Inspect submitted images
Analyze one or more user-submitted images for visual evidence relevant to the claim.
Assess evidence sufficiency
Decide whether the image evidence is sufficient to support or contradict the claim.
Identify issue type
Classify the visible issue — damage, wrong item, missing item, fraud, or food quality.
Identify object part
Pinpoint the affected component — screen, packaging, item body, accessory, or unknown.
Determine claim status
Decide whether the claim is supported, contradicted, or lacks enough information.
Select supporting image IDs
Select the specific image IDs that back the decision for audit and traceability.
Flag quality and risk signals
Flag image quality issues, category mismatches, authenticity concerns, or user-history risks.
Estimate severity
Assign severity level — low, medium, high, critical, or none — to prioritize triage.
Produce justifications
Write short, evidence-grounded reasoning tied directly to what is observable in the images.
Three claims. One engine.
See how the system processes real dispute scenarios end-to-end.
Food Order — Missing Items
{
"claim_status": "supported",
"confidence": 0.92,
"issue_type": "food_quality",
"object_part": "accessory",
"severity": "low",
"fraud_assessment": {
"fraud_score": 0.05,
"flags": []
},
"final_reasoning": "Image confirms 1
burger in bag, no fries visible.
Matches claim text."
}Electronics — Cracked Screen
{
"claim_status": "supported",
"confidence": 0.97,
"issue_type": "damage",
"object_part": "screen",
"severity": "high",
"supporting_evidence": {
"image_ids": ["1", "2"],
"valid_images": true
},
"fraud_assessment": {
"fraud_score": 0.08,
"flags": []
},
"final_reasoning": "Both images show
a large fracture across the TV
display. Crack pattern consistent
with shipping damage."
}Wrong Item — Different Brand/Model
{
"claim_status": "supported",
"confidence": 0.94,
"issue_type": "wrong_item",
"object_part": "item_body",
"severity": "medium",
"supporting_evidence": {
"image_ids": ["1"],
"valid_images": true
},
"fraud_assessment": {
"fraud_score": 0.35,
"flags": [
"abnormal_return_pattern"
]
},
"final_reasoning": "Image shows a
non-Samsung device. Claim valid,
but user has 3 past claims and 1
fraud flag — escalate for review."
}Deterministic JSON output
Every response follows the same schema — predictable, machine-readable, auditable.
claim_statussupported | contradicted | not_enough_informationconfidence0.0 – 1.0 scorefraud_score0.0 – 1.0 risk ratingissue_typedamage | wrong_item | missing_item | fraud | food_qualityseveritylow | medium | high | critical | nonefinal_reasoningEvidence-grounded explanationCatches what humans miss.
The system assigns risk signals when suspicious patterns emerge — repeated claims, inconsistent damage, stock image reuse, or category mismatches.
Fraud score ranges from 0.0 to 1.0, enabling trust & safety teams to prioritize cases by risk severity.
claims-resolution-ai
Accuracy at scale.
Deploy an AI adjudication engine that processes disputes with evidence discipline and zero hallucination.
Learn More