Vaibhav Goyal
Microsoft Engineer | Ex-Oracle | Building AI-Powered & High-Performance Applications | 2 U.S. Patents
Hyderābād, India
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Vaibhav Goyal is a Software Engineer II at Microsoft, where he works on large-scale productivity experiences, focusing on performance engineering, telemetry, caching strategies, and AI-powered features. His work involves solving latency, reliability, and scalability challenges for millions of users.
Before Microsoft, Vaibhav spent over four years at Oracle building enterprise-scale backend and release engineering systems. He has a strong interest in distributed systems, developer productivity, Generative AI, RAG architectures, and performance optimization.
Vaibhav is the inventor of two U.S. technology patents and frequently explores practical applications of AI, intelligent caching, and scalable system design. He enjoys sharing real-world engineering lessons, architectural trade-offs, and production insights that developers can directly apply to their own products.
Outside of work, he enjoys squash, running, and experimenting with emerging AI technologies.
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Beyond Prompts: Building Fast and Cost-Efficient AI Apps in Flutter
Most Flutter AI talks focus on connecting a model and displaying responses. The real challenge begins after that.
As AI features move into production, teams quickly encounter new problems: slow response times, rising token costs, inconsistent outputs, excessive context growth, and poor user experience on unreliable networks.
In this session, we'll dive into the engineering patterns used to build scalable AI-powered applications in Flutter. We'll explore how to design efficient prompt and context pipelines, implement intelligent response caching, reduce LLM costs, improve perceived performance, and instrument AI features with meaningful telemetry.
Using real-world architectural patterns inspired by production-scale applications, we'll walk through practical approaches for building responsive and cost-effective AI experiences without sacrificing quality.
Whether you're adding a simple assistant to your app or building an AI-first product, you'll leave with a set of proven techniques that can dramatically improve performance, reliability, and user satisfaction.
What you'll learn
- Design scalable prompt and context management architectures
- Reduce AI response latency through intelligent caching strategies
- Optimize token usage and lower LLM operating costs
- Measure AI feature quality using telemetry and observability
- Build resilient AI experiences that gracefully handle network and service failures
AI Apps Are Easy. Production AI Apps Are Hard.
Everyone can connect an LLM to a Flutter app in a weekend. The real challenge begins when that app reaches real users.
How do you reduce AI response latency? Control token costs? Handle unreliable networks? Reuse previous responses safely? Measure AI quality in production? Build experiences that feel instantaneous despite expensive backend models?
In this fast\-paced talk, I'll share practical engineering patterns used in large\-scale software systems that can be applied to AI\-powered Flutter applications. Drawing from experience building performance\-sensitive products at Microsoft and previously at Oracle, we'll explore how caching, context engineering, observability, and system design impact the success of AI features far more than the choice of model itself.
Attendees will leave with concrete techniques to build AI\-powered Flutter applications that are faster, cheaper, more reliable, and ready for production.
Key takeaway: Building an AI demo is easy. Building a production\-ready AI experience is an engineering challenge.
5 Seconds to 500ms: Performance Engineering Patterns for Flutter Apps at Scale
Flutter apps often start fast in development but become sluggish as features, analytics, AI integrations, and business logic grow.
This session dives deep into practical performance engineering techniques used in large-scale production systems and shows how those lessons can be applied to Flutter applications.
Instead of focusing on isolated optimizations, we'll look at performance as a system-wide discipline: startup performance, rendering efficiency, data loading strategies, caching, telemetry, and observability.
Attendees will learn how to identify bottlenecks before users notice them, measure what actually matters, and build a performance-first engineering culture.
Whether you're building a startup app or maintaining a large enterprise product, you'll leave with practical techniques that can significantly improve responsiveness, reliability, and user experience.
Vaibhav Goyal
Microsoft Engineer | Ex-Oracle | Building AI-Powered & High-Performance Applications | 2 U.S. Patents
Hyderābād, India
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