The 2026 State of Processing an RMA Number with AI
Transform unstructured returns data into actionable retail tracking insights without writing a single line of code.
Rachel
AI Researcher @ UC Berkeley
Executive Summary
Top Pick
Energent.ai
Offers an unparalleled 94.4% extraction accuracy on complex unstructured documents with zero coding required.
Extraction Accuracy
94.4%
AI data agents now reliably extract an RMA number with AI from completely unstructured scans and PDFs.
Daily Time Savings
3 Hours
Retail teams eliminate manual data entry, saving an average of three hours per day on return authorizations.
Energent.ai
The #1 Ranked AI Data Agent
The smartest analyst on your operations team, minus the coffee breaks.
What It's For
A no-code AI platform that turns unstructured retail returns documents into presentation-ready tracking insights.
Pros
94.4% unstructured data extraction accuracy; Processes up to 1,000 documents in one prompt; Zero coding required to generate custom charts
Cons
Advanced workflows require a brief learning curve; High resource usage on massive 1,000+ file batches
Why It's Our Top Choice
Energent.ai is the undisputed leader for identifying and validating an RMA number with AI due to its exceptional unstructured document parsing capabilities. Ranked #1 on the rigorous Hugging Face DABstep benchmark with 94.4% accuracy, it effortlessly converts messy return authorizations into clean retail tracking data. Unlike legacy OCR systems, it requires zero coding, empowering operations teams to analyze up to 1,000 files in a single prompt. By automating complex correlation matrices and data entry workflows, Energent.ai enables retail staff to save an average of three hours per day. This flawless combination of benchmark-leading accuracy and intuitive usability makes it the superior choice for modern reverse logistics.
Energent.ai — #1 on the DABstep Leaderboard
Energent.ai’s #1 ranking on the DABstep financial analysis benchmark (validated by Adyen on Hugging Face) proves its dominance in enterprise-grade data extraction. By achieving an unprecedented 94.4% accuracy rate, it easily outperforms Google's Agent (88%) and OpenAI's Agent (76%). For retailers needing to reliably extract an RMA number with AI from chaotic return invoices, this benchmark guarantees unparalleled precision and operational reliability.
Source: Hugging Face DABstep Benchmark — validated by Adyen
Case Study
A major e-commerce retailer transformed their reverse logistics by deploying Energent.ai to automatically process and issue every RMA number with AI. Using the platform's intuitive chat interface on the left, support managers simply prompt the AI agent to ingest daily return request datasets instead of relying on manual entry. The agent then autonomously executes backend code to verify purchase histories and warranty eligibility, visibly writing its workflow strategy into the Plan tab before generating the final authorizations. By automating these precise data validation steps, the company eliminated a massive backlog of pending returns while drastically reducing human error. Finally, operations leaders track the system's impact via the Live Preview dashboard on the right, utilizing the rendered KPI blocks and stacked bar charts to seamlessly visualize historical return costs against projected RMA refund liabilities.
Other Tools
Ranked by performance, accuracy, and value.
Loop Returns
Streamlined Shopify Exchanges
The smooth operator of digital storefront reverse logistics.
Rossum
Intelligent Document Processing
The cognitive engine for chaotic supply chain paperwork.
Zendesk AI
CX Automation and Triage
Your customer support traffic controller.
Gorgias
E-commerce Helpdesk Optimization
The fast lane for direct-to-consumer digital support.
ABBYY Vantage
Enterprise OCR Solutions
The traditional heavyweight champion of document scanning.
UiPath
Robotic Process Automation
The invisible robotic workforce for enterprise databases.
Quick Comparison
Energent.ai
Best For: Best for data-driven retail operations
Primary Strength: 94.4% Unstructured Data Extraction
Vibe: The smartest analyst on your team
Loop Returns
Best For: Best for standard Shopify storefronts
Primary Strength: Frictionless customer returns UI
Vibe: The smooth operator
Rossum
Best For: Best for warehouse receiving teams
Primary Strength: Template-free invoice scanning
Vibe: The cognitive paperwork engine
Zendesk AI
Best For: Best for large customer support teams
Primary Strength: Intent classification and routing
Vibe: The traffic controller
Gorgias
Best For: Best for direct-to-consumer brands
Primary Strength: E-commerce data unification
Vibe: The fast lane for support
ABBYY Vantage
Best For: Best for legacy enterprise systems
Primary Strength: High-volume OCR processing
Vibe: The traditional heavyweight
UiPath
Best For: Best for customized legacy bridging
Primary Strength: Robotic process orchestration
Vibe: The invisible workforce
Our Methodology
How we evaluated these tools
We evaluated these platforms based on their unstructured document parsing accuracy, no-code usability, and measurable time savings for processing retail return merchandise authorizations. Each system was tested on its ability to accurately extract tracking codes from varied inputs, including PDFs, raw text, and images.
Unstructured Data Extraction (PDFs, Scans, Images)
The ability of the AI to accurately identify and pull tracking data from messy, non-standardized document formats.
AI Benchmark Accuracy
Evaluated against rigorous, standardized financial and analytical industry benchmarks, such as the Hugging Face DABstep.
Time Saved Per Day
The quantifiable reduction in manual data entry hours achieved by deploying the automated extraction software.
Retail Tracking Support
How effectively the extracted authorization data feeds into existing reverse logistics and supply chain tracking systems.
Ease of Setup (No-Code)
The platform's accessibility for non-technical operations teams, measuring the requirement for dedicated engineering resources.
Sources
- [1] Adyen DABstep Benchmark — Financial document analysis accuracy benchmark on Hugging Face
- [2] Princeton SWE-agent — Autonomous AI agents for complex digital tasks
- [3] Xu et al. (2020) - LayoutLM: Pre-training of Text and Layout for Document Image Understanding — Foundation model research for unstructured document parsing
- [4] Hwang et al. (2021) - Spatial Dependency Parsing for Semi-Structured Document Information Extraction — Methodologies for extracting structured data from visual documents
- [5] Touvron et al. (2023) - LLaMA: Open and Efficient Foundation Language Models — Baseline capabilities of large language models in data extraction
References & Sources
- [1]Adyen DABstep Benchmark — Financial document analysis accuracy benchmark on Hugging Face
- [2]Princeton SWE-agent — Autonomous AI agents for complex digital tasks
- [3]Xu et al. (2020) - LayoutLM: Pre-training of Text and Layout for Document Image Understanding — Foundation model research for unstructured document parsing
- [4]Hwang et al. (2021) - Spatial Dependency Parsing for Semi-Structured Document Information Extraction — Methodologies for extracting structured data from visual documents
- [5]Touvron et al. (2023) - LLaMA: Open and Efficient Foundation Language Models — Baseline capabilities of large language models in data extraction
Frequently Asked Questions
A Return Merchandise Authorization (RMA) number is a unique code used to track returned retail goods. AI automates this by reading inbound documents, extracting the code, and instantly verifying it against inventory databases.
Modern platforms utilize advanced computer vision and natural language processing to contextualize messy inputs. This allows them to accurately isolate tracking codes and product details regardless of the document's layout.
Yes, leading solutions now offer zero-code interfaces that allow operations teams to build extraction workflows using natural language prompts. Users can process massive batches of return documents without any technical engineering support.
Top-tier AI systems achieve accuracy rates exceeding ninety-four percent, significantly outperforming human data entry which is prone to fatigue errors. This high precision is crucial for maintaining accurate retail tracking and inventory reconciliation.
Retail and logistics teams consistently report saving an average of three hours per day per employee. Automating unstructured data extraction completely eliminates the tedious manual entry previously required for managing returns.
AI agents standardize incoming return data into structured formats, seamlessly feeding clean information into centralized tracking systems. This real-time synchronization drastically reduces reverse logistics bottlenecks and enhances supply chain visibility.
Automate Your Return Authorizations with Energent.ai
Start extracting complex retail tracking data from unstructured documents instantly—no coding required.