Company Profile
Company Type
Ecommerce marketplace seller
Team Size
30-150 employees
Industry
E-Commerce
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was order fulfillment speed.
Tools Connected
The Challenge
This ecommerce marketplace seller was trapped in a data entry nightmare. Every day, their team of 30-150 employees received e-commerce-specific documents in dozens of formats — PDFs, scanned images, spreadsheets, emails, and handwritten forms. Each document required manual extraction and entry into multiple systems, with the average record taking 8-12 minutes to process completely.
The cost was staggering. Between direct labor ($85K+ annually in data entry staffing), error correction costs, and the opportunity cost of delayed data availability, the organization estimated they were spending over $150K per year on what was essentially a solved problem. Worse, the manual process created a 48-hour lag between document receipt and data availability, meaning their e-commerce team was always working with outdated information. Critical decisions were being made based on data that was days old.
The Solution
Arahi AI gave this e-commerce team the data entry automation they needed. The implementation connected their existing tools — Shopify, Mailchimp, and Klaviyo — and deployed AI agents that could understand, extract, and validate data from any e-commerce document type they received.
The key innovation was the validation layer. Rather than just extracting data and hoping for the best, the AI agents cross-reference every extracted field against e-commerce-specific business rules, historical patterns, and related records in the system. Duplicate detection catches records that already exist, format validation ensures data consistency, and anomaly detection flags values that fall outside expected e-commerce ranges. The result is data that enters their systems clean, accurate, and ready for use — without any human touching a keyboard.
The Results
Measurable improvements across key e-commerce data entry metrics.
Processing Time per Record
Dramatically faster
Before
Minutes
After
Seconds
Error Rate
Major reduction
Before
Noticeable
After
Minimal
Data Availability Lag
Near real-time
Before
Days
After
Same day
Annual Labor Cost
Major savings
Before
High
After
Fraction of manual cost
Processing Capacity
Massive throughput increase
Before
Limited
After
Dramatically higher
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our e-commerce data entry workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Ecommerce marketplace seller
Key Takeaways
The most important lessons from this e-commerce data entry automation project.
AI-powered data entry automation dramatically reduced manual processing time for this e-commerce team, freeing staff to focus on high-value strategic work.
Implementation took less than a day — the no-code approach meant no IT bottleneck or months-long development cycle.
Error rates dropped significantly, improving data quality and downstream decision-making.
The ROI was realized quickly, with the solution paying for itself through cost savings and productivity gains.
Implementation Timeline
From zero to production in Half a day — here's how they did it.
Step 1: Mapped the existing data entry workflow
Documented every step of the current manual data entry process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the data entry workflow: connected Shopify and ShipStation as data sources, configured AI decision logic for e-commerce-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on the vast majority of data entry instances, with edge cases automatically flagged for human review.
Setup Time
Half a day
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating data entry in e-commerce.
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This case study represents a typical customer scenario. Individual results may vary.