At a Glance: A Zurich-based InsurTech company with $22M in annual revenue hired 4 senior backend engineers through Second Talent to rebuild their claims processing infrastructure and integrate AI-powered underwriting systems. The AI-native team, based in Indonesia and Vietnam with 8+ years of experience each, delivered a complete API overhaul that reduced response times by 70% and cut engineering costs by $350K annually.
The Challenge
The company’s legacy claims processing system was buckling under growth. Their monolithic architecture handled 2,400 concurrent policies at peak. By early 2025, they needed to support 25,000. Response times averaged 3.2 seconds per API call. Customers abandoned claims submissions during processing delays.
The technical debt was severe. Their existing backend mixed Python 2.7 code with newer Go services. No unified API layer existed. Each insurance product line had separate processing logic. Manual underwriting consumed 18 hours per complex commercial policy. The engineering team spent 60% of their time on maintenance instead of new features.
Hiring in Zurich proved impossible at their budget. Senior backend engineers with Go and Python expertise commanded $140K to $180K annually plus Swiss benefits. The company received 11 applications over 4 months. Only 2 candidates had the distributed systems experience they needed. Both declined after receiving competing offers. The CTO had 90 days to deliver a scalable solution before their largest enterprise client renewal.
They needed engineers who could architect modern microservices, integrate AI models for underwriting automation, and ship production code fast. Local talent acquisition was not going to meet their timeline or budget constraints.
The Solution
Second Talent presented 12 qualified candidates within 8 days. All had 8+ years of backend experience. All were AI-native developers who actively used LLMs for code generation, testing, and documentation. The company interviewed 6 candidates and selected 4 senior engineers, 2 based in Jakarta and 2 in Ho Chi Minh City.
The hiring process took 19 days from first contact to signed contracts. Second Talent handled all employment paperwork through EOR services in Indonesia and Vietnam. The developers started on the same day. No entity setup required. No local compliance burden. The company paid a single monthly invoice covering salaries, benefits, and EOR fees.
Each developer earned between $5,200 and $6,800 monthly, reflecting their senior expertise and AI proficiency. They worked European hours with 4-hour overlap with the Zurich office. The team used GitHub Copilot, Cursor, and Claude for rapid development. They shipped the first microservice migration in 11 days.
The developers rebuilt the claims API using Go with a Python layer for AI model integration. They implemented event-driven architecture using Kafka. They containerized everything with Kubernetes. AI models analyzed claim documents, extracted data, and flagged fraud patterns automatically. The team wrote comprehensive tests using AI-generated test cases that covered 847 edge conditions the previous manual QA had missed.
The Results
API response times dropped from 3.2 seconds to 0.9 seconds, a 70% improvement. The new architecture handled 24,000 concurrent policies during load testing, 10x the previous capacity. Claims processing that took 4.5 days now completed in 21 hours, a 5x speed increase. The AI underwriting system reduced manual review time from 18 hours to 3.2 hours per complex policy.
The financial impact was immediate. The company saved $350K annually compared to hiring equivalent talent in Switzerland. The 4 developers cost $26,400 monthly including EOR fees. Four comparable Zurich hires would have cost $55,500 monthly in salary alone, before benefits and office costs. The faster claims processing increased customer satisfaction scores from 6.8 to 8.9 out of 10.
Code quality improved measurably. The AI-native developers maintained 94% test coverage versus 67% previously. They documented every API endpoint with AI-generated examples. Deployment frequency increased from twice monthly to 3 times per week. The CTO reported zero production incidents in the first 90 days after launch, compared to 14 incidents in the prior quarter.
Key Outcomes
- Infrastructure Performance: API response times decreased 70% from 3.2 to 0.9 seconds while supporting 10x more concurrent policies, scaling from 2,400 to 24,000 active connections.
- Processing Efficiency: Claims processing accelerated 5x from 4.5 days to 21 hours, and AI-powered underwriting cut manual review time from 18 hours to 3.2 hours per complex commercial policy.
- Cost Optimization: Annual engineering costs dropped $350K compared to Swiss market rates, with the 4-developer team costing $26,400 monthly versus $55,500 for local equivalents before benefits.
- Quality and Velocity: Test coverage increased from 67% to 94%, deployment frequency rose from 2 to 12 times monthly, and production incidents dropped from 14 to zero in the first 90 days post-launch.
“We went from drowning in technical debt to shipping features our enterprise clients actually wanted. The team Second Talent provided understood distributed systems and AI integration from day one. They reduced our API response time by 70% and saved us $350K annually. That combination of speed, quality, and cost efficiency would have been impossible with local hiring.”
VP of Engineering
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