Company Profile
Company Type
Precision parts manufacturer
Team Size
50-250 employees
Industry
Manufacturing
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was quality control.
Tools Connected
The Challenge
This precision parts manufacturer was trapped in a data entry nightmare. Every day, their team of 50-250 employees received manufacturing-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a 48-hour lag between document receipt and data availability, meaning their manufacturing team was always working with outdated information.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 manufacturing team the data entry automation they needed. The implementation connected their existing tools — SAP, Google Sheets, and Gmail — and deployed AI agents that could understand, extract, and validate data from any manufacturing 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 manufacturing-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 manufacturing 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 manufacturing 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
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our manufacturing 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
Precision parts manufacturer
Key Takeaways
The most important lessons from this manufacturing data entry automation project.
AI-powered data entry automation eliminated 88% of manual processing time for this manufacturing 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 by over 90%, significantly improving data quality and downstream decision-making.
The ROI was realized within the first month, with the solution paying for itself multiple times over 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 SAP and Slack as data sources, configured AI decision logic for manufacturing-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 98% of data entry instances, with the 2% of 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 manufacturing.
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