AI Multilingual Chatbot for Financial Assistance
24/7 multilingual
support enabled through an AI chatbot.
Lower operational costs
achieved by automating FAQs and loan pre-qualification assistance.
Industries
Industries We Serve
Cross-industry engineering experience across regulated and high-growth sectors.
All IndustriesTechnologies
Ruby
Ruby on Rails
PostgreSQL
Expertise
Back-end engineering
Highload
Data analysis
Legacy modernization
Client Overview
Leading Construction SaaS Provider
The client is a recognized player in the construction technology space. The company delivers SaaS solutions that digitize and streamline construction management processes. Its cloud-native platform supports organizations across both construction operations and technology-driven environments.
Industries:
Construction SaaS, Project management software
Country:
USA
Challenges
Performance Bottlenecks Slowing Workflows
As the client’s finance tool grew in data volume, list views became sluggish—slowing sorting, calculations, and day-to-day operational workflows for key enterprise clients.
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Accelerate system performance with expert backend optimization.
Slow data operations
Sorting financial columns and calculating totals took seconds or minutes on large projects, frustrating users.
Backend inefficiencies
Legacy data access patterns and N+1 queries overloaded the server and limited scalability.
Client impact
Delays in viewing or aggregating financial data affected productivity for the client’s largest, most data-heavy customers.
Scalability concerns
The existing logic struggled to meet performance expectations as datasets and active users expanded.
Have a Similar Problem?
Accelerate system performance with expert backend optimization.
Why They Chose Us
Expertise in High-Impact Performance Engineering
The client selected Zoolatech for our deep understanding of complex data systems and our ability to deliver measurable speed improvements without disrupting active users.
Engineering excellence
Zoolatech brings full-stack expertise across modern architectures, delivering clean, efficient, and maintainable solutions that scale with business growth.
Collaborative delivery model
We integrate seamlessly with client teams, combining technical depth with transparent communication and shared ownership of results.
Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.
2017
Year Founded
600+
Employees
96%
Client Satisfaction
Workflow
Structured Optimization for Lasting Performance Gains
Zoolatech combined deep code analysis with agile delivery to enhance performance without disrupting production systems.
Phase 1
Analysis and benchmarking
Profiled the finance tool's list view to identify performance bottlenecks and quantify query inefficiencies.
Phase 2
Data layer refactoring
Redesigned backend logic and SQL execution paths, replacing inefficient code with resource-based access models.
Phase 3
Optimization and validation
Eliminated N+1 queries, offloaded calculations to the database, and tested performance improvements across large datasets.
Phase 4
Deployment and monitoring
Rolled out upgrades incrementally, validating stability through continuous monitoring and feedback from enterprise users.
Phase 5
Knowledge transfer
Documented best practices and implementation steps to support future scalability across related client modules.
A legacy list view was rebuilt with optimized SQL and database-level processing, delivering up to 42x faster sorting and 30x faster aggregations—unlocking real-time performance for enterprise financial workflows.
Solution
Redesigned for Speed, Stability, and Scale
Zoolatech expert refactored the client’s financial tool's list view to handle enterprise-level data volumes with ease and precision.
Optimized data access layer
Introduced resource-based SQL queries and removed N+1 patterns to drastically reduce load times and memory overhead.
Database-side aggregation
Shifted heavy financial calculations from the app layer to the database, achieving over 30× faster response times.
Server-side rendering upgrade
Implemented efficient backend rendering logic that improved sorting speed by up to 42x across large datasets.
Risks and Mitigations
Balancing Speed, Accuracy, and Stability
We proactively managed performance and deployment risks to ensure reliable improvements in production environments.
Option |
Risk |
Mitigation |
|---|---|---|
| Data integrity risks | Refactoring SQL logic could affect financial calculations and reporting accuracy. | Implemented regression tests and cross-validation of query results before production rollout. |
| Performance regression | Optimizations might improve some operations but degrade others under high load. | Benchmarked endpoints across multiple datasets and monitored response times post-deployment. |
| Deployment stability | Rolling out database-heavy changes could disrupt live user sessions. | Used phased deployment with rollback safety and continuous monitoring. |
| Scalability alignment | Localized optimizations might not scale to other modules or new data models. | Documented reusable patterns and shared performance principles with the client’s internal teams. |
Results
Faster Sorting and a Smoother User Experience
The refactored architecture transformed how the client’s users interact with large financial datasets—delivering instant responsiveness and measurable gains in efficiency.
42x faster sorting speed
The implementation of the customized ERP system resulted in a fivefold reduction in operational costs.
30x faster totals calculation
Aggregations moved to the database reduced endpoint latency and boosted overall responsiveness.
Zero production downtime
Incremental deployment ensured continuous availability and user confidence throughout optimization.
Business Value
Delivering Speed and Confidence at Enterprise Scale
With faster list views and stable backend logic, our client can now support data-heavy clients with consistent performance and reliability.
Enhanced user productivity
Faster response times and smoother workflows improved daily efficiency for teams managing large-scale financial data.
Foundation for future optimization
The new architecture established reusable performance patterns that can be extended across other client’s modules.
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