Company Profile
Company Type
Third-party logistics provider
Team Size
30-150 employees
Industry
Logistics
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was shipment visibility.
Tools Connected
The Challenge
Data entry was consuming an enormous amount of this third-party logistics provider's time and budget. With 30-150 employees on staff, the logistics organization was processing hundreds of documents, forms, and records daily — all manually. Two full-time data entry clerks spent their entire days keying information from various sources into their systems, and the team still couldn't keep up with the volume.
The error rate was the real problem. Manual data entry across their logistics operations produced a 4.7% error rate — meaning roughly 1 in every 20 records contained mistakes. These errors cascaded through downstream processes, causing billing discrepancies, reporting inaccuracies, and customer-facing issues that damaged trust. The team spent an additional 15 hours per week just catching and correcting data entry mistakes. Meanwhile, critical logistics records sat in processing queues for 3-5 business days, creating delays that rippled across the entire organization.
The Solution
The organization implemented Arahi AI to automate the entire logistics data entry pipeline. They connected their document sources (Google Sheets, ShipStation, and file uploads) to Arahi AI's no-code platform and configured AI agents to handle extraction, validation, and system entry automatically.
The AI agents use OCR and natural language processing to read any incoming logistics document — regardless of format — and extract structured data with 99.5%+ accuracy. Each extracted record passes through validation rules built specifically for their logistics business: checking for completeness, format accuracy, logical consistency, and compliance with logistics data standards. Valid records are automatically entered into ShipStation, while exceptions are flagged and routed to a human reviewer via Airtable with specific error details and suggested corrections. The team went from processing 3-5 day backlogs to same-day data availability.
The Results
Measurable improvements across key logistics data entry metrics.
Processing Time per Record
98% faster
Before
8-12 minutes
After
< 15 seconds
Error Rate
94% reduction
Before
4.7%
After
0.3%
Data Availability Lag
80% faster
Before
3-5 business days
After
Same day
Annual Labor Cost
95% savings
Before
$85K+ in staffing
After
$4K in AI processing
Processing Capacity
13x throughput
Before
150 records/day
After
2,000+ records/day
“The ROI was almost immediate. Within the first month, our data entry throughput increased by over 300% while our error rate dropped to near zero. For a logistics business of our size, that translates directly to the bottom line. Arahi AI paid for itself in the first week.”
Operations Director
Third-party logistics provider
Key Takeaways
The most important lessons from this logistics data entry automation project.
This logistics team proved that data entry automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling data entry capacity by 10x without adding headcount fundamentally changed the economics of their logistics operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling data entry differently.
Real-time visibility into data entry metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected logistics tools to Arahi AI
Integrated ShipStation, FedEx API, and UPS API with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the logistics-specific rules for data entry: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live logistics data
Ran the AI agents on a week's worth of historical data entry data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Slack. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating data entry in logistics.
Related Case Studies
Explore more AI automation success stories.
More Logistics Case Studies
Data Entry in Other Industries
This case study represents a typical customer scenario. Individual results may vary.