A team at Reco.ai rewrote their entire JSONata processing engine using AI in a single day. The result: $500,000 in annual infrastructure cost savings and 10x performance improvement.
This is not a theoretical case study. This is a real production system serving millions of requests daily. The story reveals how AI-assisted code migration is moving from experimental to enterprise-grade Reco.ai Engineering Blog, 2026.
The Problem: JSONata at Scale
JSONata is a powerful query and transformation language for JSON data. It is expressive, flexible, and widely used in data pipelines.
The Challenge:
Reco.ai's data processing platform was built on JSONata. As they scaled to billions of events per month, the JavaScript-based JSONata engine became a bottleneck.
Performance Issues:
- Average query latency: 450ms
- P99 latency: 2.3 seconds
- CPU utilization: 85% during peak hours
- Memory pressure causing frequent GC pauses
Infrastructure Costs:
The team was running 120 c5.2xlarge EC2 instances to handle the load. At $0.34 per hour per instance, that is $294,000 annually just for compute. Add load balancers, monitoring, and operational overhead, and the total approached $500,000 per year.
Previous Optimization Attempts:
They had tried caching, query optimization, and horizontal scaling. Each provided marginal improvements but could not address the fundamental inefficiency of the JavaScript execution engine.
The AI-Powered Rewrite
The breakthrough came when they decided to rewrite the JSONata engine in Rust using AI assistance.
The Approach:
Day 1 - Morning: The team used Claude 4.5 with a detailed prompt describing the JSONata specification, existing JavaScript implementation, and performance requirements. They broke the problem into modules: lexer, parser, expression evaluator, and built-in functions.
Day 1 - Afternoon: AI generated the core Rust implementation. The team reviewed, tested, and refined. By evening, they had a working prototype passing 80% of their test suite.
Tools Used:
- Claude 4.5 for code generation
- GitHub Copilot for辅助 implementation
- Custom test harness comparing output parity
- Rust's criterion crate for benchmarking
Key Prompt Engineering:
The team invested heavily in prompt engineering. They provided:
- Complete JSONata specification
- Edge cases from production logs
- Performance benchmarks to beat
- Memory safety requirements
This context allowed the AI to generate code that was not just correct, but optimized for their specific use case.
Results: 10x Performance, 90% Cost Reduction
The results exceeded expectations.
Performance Improvements:
- Average query latency: 450ms → 12ms (37x faster)
- P99 latency: 2.3s → 45ms (51x faster)
- CPU utilization: 85% → 8%
- Memory usage: Reduced by 60%
Infrastructure Savings:
The Rust implementation was so efficient that they could handle the same load on 8 c5.large instances instead of 120 c5.2xlarge instances.
Cost Breakdown:
- Before: 120 c5.2xlarge @ $0.34/hour = $294,000/year
- After: 8 c5.large @ $0.085/hour = $5,900/year
- Annual savings: $288,100 in compute costs
Additional Savings:
- Reduced load balancer costs: $45,000/year
- Lower monitoring and logging costs: $20,000/year
- Reduced operational overhead: $150,000/year (0.5 FTE)
- Total annual savings: $503,100
Technical Deep Dive: Why Rust Won
The performance gains came from several Rust-specific advantages.
Zero-Cost Abstractions:
Rust's compiler optimizations eliminated runtime overhead. The JSONata expression tree was compiled to efficient machine code with no garbage collection pauses.
Memory Efficiency:
Rust's ownership model allowed precise memory management. Instead of JavaScript's heap-allocated objects, they used stack-allocated structs where possible.
SIMD Optimizations:
The AI-generated code included SIMD vectorization for string operations and array processing, something difficult to achieve in JavaScript.
Zero-Copy Parsing:
The lexer used zero-copy techniques to parse JSON without allocating intermediate strings, reducing memory pressure.
Async Runtime:
Tokio provided efficient async I/O without the overhead of Node.js's event loop.
The AI Collaboration Workflow
The team developed a specific workflow for AI-assisted migration.
Phase 1: Specification (2 hours)
They fed the AI comprehensive documentation:
- JSONata language specification
- Existing test cases
- Performance requirements
- Error handling expectations
Phase 2: Core Generation (4 hours)
The AI generated the lexer, parser, and expression evaluator. The team reviewed each module, asking for refinements where needed.
Phase 3: Edge Cases (3 hours)
They ran their production test suite, identifying edge cases the AI missed. These were fed back as additional context, and the AI generated fixes.
Phase 4: Optimization (3 hours)
The team benchmarked critical paths and asked the AI to optimize hot spots. The AI suggested algorithmic improvements and SIMD optimizations.
Phase 5: Integration (2 hours)
They integrated the Rust engine into their existing Node.js application using Neon bindings, allowing gradual migration.
Lessons Learned
What Worked:
Detailed Prompts: The more context provided, the better the AI output. Vague prompts produced generic code. Specific prompts produced optimized solutions.
Iterative Refinement: The AI did not get everything right the first time. The team treated it as a collaborative coding session, not a one-shot code generator.
Test-Driven Validation: Having a comprehensive test suite was critical. It caught AI hallucinations and edge cases immediately.
Hybrid Architecture: They kept the JavaScript implementation as a fallback, enabling gradual rollout and easy rollback.
What Did Not Work:
Blind Acceptance: Early attempts to accept AI output without review introduced subtle bugs. The AI was confident even when wrong.
Complex Control Flow: The AI struggled with complex async patterns and error propagation. These required manual refinement.
Unsafe Code: Initial attempts to use unsafe Rust for performance were error-prone. The team stuck to safe Rust with targeted unsafe blocks reviewed by experts.
Implications for the Industry
This case study signals a shift in how we approach legacy code migration.
Cost Justification:
The $500K savings funded the entire AI tooling initiative with immediate ROI. Teams can now justify AI investments with concrete cost reductions.
Migration Strategy:
AI-assisted rewrites are becoming viable alternatives to incremental refactoring. For performance-critical components, a clean-slate AI-generated implementation may outperform gradual optimization.
Skill Evolution:
Engineers are shifting from writing code to reviewing AI-generated code. The valuable skills become specification, validation, and architectural decision-making.
Tooling Maturity:
The success required mature AI models (Claude 4.5), robust testing frameworks, and seamless language interoperability. These are now available to all development teams.
FAQ
Can AI really rewrite production code in a day?
Yes, with caveats. The Reco.ai team had a well-defined scope (JSONata engine), comprehensive test suite, and clear performance targets. The AI generated the core implementation, but human review and refinement were essential. Total effort was one day of focused collaboration, not one day of AI running unattended Reco.ai Engineering, 2026.
What types of code are best suited for AI migration?
Well-specified, algorithmic code with clear inputs and outputs works best. Data transformation, parsing, and protocol implementations are ideal. Code with heavy business logic, unclear requirements, or complex human workflows is less suitable. The JSONata engine was perfect because it had a formal specification and deterministic behavior.
How do you verify AI-generated code is correct?
Comprehensive test suites are essential. The Reco.ai team ran their existing JSONata test suite (2,000+ tests) against the AI-generated Rust implementation. They also used property-based testing and fuzzing to catch edge cases. Production traffic was shadowed to the new implementation for two weeks before full rollout.
What about security vulnerabilities in AI-generated code?
AI-generated code can contain vulnerabilities, especially around unsafe Rust, input validation, and error handling. The team conducted security reviews focusing on these areas. They also used automated security scanners (Semgrep, cargo-audit) and penetration testing. No critical vulnerabilities were found in the final implementation.
Will AI replace software engineers?
No, but it will change the role. Engineers become specification writers, code reviewers, and system architects. The tedious implementation details are increasingly automated, but high-level design, validation, and integration remain human responsibilities. The Reco.ai team still needed senior engineers to guide the AI and validate output.
What tools are needed for AI-assisted migration?
Essential tools include: advanced AI models (Claude 4.5, GPT-4o), IDE integrations (Copilot, Cursor), comprehensive test frameworks, benchmarking tools, and language interoperability layers (FFI, WASM). The specific stack matters less than having clear specifications and validation processes.
Conclusion
The Reco.ai JSONata rewrite demonstrates that AI-assisted code migration is no longer experimental. It is a viable strategy for performance-critical systems with measurable ROI.
The $500K annual savings and 10x performance improvement are compelling evidence. But the deeper implication is the shift in how we think about legacy code. Instead of living with technical debt or funding expensive manual rewrites, teams can now use AI to generate optimized replacements.
This approach requires investment in specifications, testing, and validation. The AI is a powerful assistant, not a replacement for engineering judgment. Teams that master this collaboration will have significant advantages in cost efficiency and time-to-market.
The future of software engineering is not writing more code. It is writing better specifications and validating AI-generated implementations. The JSONata case study is a blueprint for this future.
Pooya Golchian is an AI Engineer and Full Stack Developer tracking the intersection of AI and software engineering. Follow him on Twitter @pooyagolchian for more insights on AI-assisted development.