Mobiele ontwikkeling
Kunstmatige intelligentie
Sifars built a custom solution using Python, OpenAI, and Vector Databases. The system monitors the client's inbox for new loan requests, downloads and reads email attachments, extracts structured data from emails and files, saves the extracted data into the CRM, and automatically assigns tickets to the relevant team.
Loan applications were arriving in high volume through email, and the process from inbox to CRM remained manual, slow, and error-prone. Existing automation had already failed, training overhead was high, and growing application volume forced the team to scale operations constantly.
Sifars built a business-first automation workflow that monitors inboxes, reads attachments, extracts structured application data with AI, syncs everything into the CRM, and routes tickets automatically. The system was designed around the client's real operating model instead of forcing AI where it did not belong.
The previous automation solution failed to meet expectations, leaving the client stuck between unreliable tooling and a manual process they could not scale with confidence.
Applications took too long to move from the inbox into the CRM. Manual handoffs created workflow delays that slowed decisions, loan disbursement, and customer response times.
Loan applications contained the same information in many different layouts, including 25-40 field variations. That made extraction error-prone and difficult to automate cleanly.
Errors in extracted data could affect loan decisions, compliance, and financial outcomes. Any automation had to be highly reliable and include strong validation mechanisms.
From inbox monitoring to CRM integration and ticket assignment, every step in LoanFlow AI was designed to remove manual work while preserving accuracy and operational control.
The system continuously monitors the client's inbox for new loan requests so every application is detected immediately and queued without manual checks.
Email attachments are automatically downloaded and prepared for processing, ensuring the platform captures all relevant files across different application formats.
OpenAI and Vector Databases extract structured application data from variable email and document layouts, handling format differences with high accuracy.
The extracted information is validated for accuracy and completeness before being saved to the CRM, eliminating manual data entry while protecting data quality.
Once the record is created, the system assigns tickets to the relevant team based on business rules so applications move forward without operational bottlenecks.
The system continuously monitors the client's inbox for new loan requests, ensuring no application is missed and kicking off a workflow tailored to the client's real intake process the moment each email arrives.
Email attachments containing loan applications are automatically downloaded and processed using logic tuned to the document patterns in the client's inbox, eliminating manual file handling and reducing turnaround time.
Using OpenAI and Vector Databases, the system intelligently extracts structured data from emails and files, handling variable formats with high accuracy regardless of how the information is arranged. A built-in validation layer checks critical fields before records move downstream.
Validated data is automatically saved into the CRM, removing manual data entry and creating consistent, accurate records for downstream processing.
The system automatically assigns tickets to the relevant team based on application type, priority, and business rules shaped around the client's operating structure, improving routing and operational flow.
LoanFlow AI increased processing capacity from 50-60 applications per day to 200+ per day, reduced manual overhead and offshore dependency, and reached production quickly with a proof of concept in 1 week and a working product in 4 weeks. The client was highly satisfied and later engaged Sifars for another product.
Solving this problem allowed the client to scale from a few dozen applications per day to hundreds, directly improving how many loans they could process and disburse.
This project shows that AI can create real value inside core business operations, not only in outward-facing chatbot and support experiences.
AI was not forced into the workflow. Sifars first understood the client's operational reality and used AI only where it improved speed, accuracy, and scale.
The engagement reinforces Sifars' ability to adapt AI systems to complex, domain-specific workflows across industries while keeping the solution grounded in business outcomes.
The client operated with 28 employees and rising month-on-month loan volume. The solution reduced the need to continuously expand manual operations and training overhead.
Sifars combined the client's functional knowledge with technical execution, delivering a proof of concept in 1 week and a working product in 4 weeks while keeping room for ongoing enhancement.
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