Company Profile
Company Type
Fintech startup
Team Size
50-250 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was data accuracy.
Tools Connected
The Challenge
This fintech startup was trapped in a data entry nightmare. Every day, their team of 50-250 employees received finance-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a 48-hour lag between document receipt and data availability, meaning their finance team was always working with outdated information.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 finance team the data entry automation they needed. The implementation connected their existing tools — QuickBooks, Stripe, and Google Sheets — and deployed AI agents that could understand, extract, and validate data from any finance 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 finance-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 finance 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 finance 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 finance business of our size, that translates directly to the bottom line. Arahi AI paid for itself in the first week.”
Operations Director
Fintech startup
Key Takeaways
The most important lessons from this finance data entry automation project.
AI-powered data entry automation eliminated 88% of manual processing time for this finance 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 QuickBooks and Plaid as data sources, configured AI decision logic for finance-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 finance.
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