Projects

Real problems. Real products. Measurable results.

Live product

Gledva

AI Travel Companion · B2C

An intelligent travel workspace: AI itinerary planning, trip timeline, bookings, documents, tasks, packing, budget, reminders, weather, maps, nearby discovery and in-trip assistance across iOS, Android and Web.

What I owned: Product strategy · UX · requirements · AI architecture · prioritization · launch · GTM · analytics

  • 100+ active users in Q1
  • 3 platforms
  • 0→1 launch
Live product

TripHub

AI SaaS for Travel Advisors · B2B

Advisor workspace covering inquiry → traveler → proposal → trip → booking → documents → tasks → communication → completion — with a cited Knowledge Hub over supplier contracts, a WhatsApp and e-mail inbox with AI-drafted replies and client memory, and AI governance each agency controls.

What I owned: Product strategy · roadmap · UX · AI workflows · SaaS pricing · onboarding · GTM

  • 20+ registered advisors
  • 100% answer accuracy in eval
  • WhatsApp + e-mail inbox
Live product

GledMD

AI Medical Education

USMLE QBank, AI mock interviews, personal statement builder, ERAS CV review, school finder, interview readiness score and IMG Hub in one platform.

What I owned: Product strategy · requirements · AI experiences · packaging · pricing · iteration · GTM

  • 50+ active users in Q1
  • 0→1 launch
  • Continuous validation
Architecture & prototyping

Voice AI

Real-Time Voice AI · Systems Architecture

A real-time voice assistant for sales conversations, rebuilt from measurement: pipelined streaming instead of six sequential steps, memory-bandwidth-driven GPU choice, container sizing for cold start and cost, and sentence-level streaming to hide synthesis latency.

What I owned: Systems architecture · latency profiling · unit economics · hardware selection · streaming design

  • 8.5s → 0.8s by GPU choice
  • 87% of cost is voice
  • 6 sequential steps pipelined
Benchmark

GPU Benchmark

Voice Inference Benchmarking · MI300X vs GH200 vs H100 vs Managed APIs

A full speech-to-text, LLM and text-to-speech pipeline on one self-hosted AMD MI300X, NVIDIA GH200 and NVIDIA H100 PCIe, measured against Azure AI Foundry and OpenAI on time to first audio, concurrency against a 500 ms SLO, shared-GPU isolation and cost per token.

What I owned: Benchmark design · latency SLOs · capacity planning · unit economics · ROCm/CUDA vLLM serving

  • 331 ms to first audio (GH200)
  • 256 calls per GPU (MI300X)
  • ~$0.08 vs $2.08 per 1M tokens
Past experience

Intel

Connected PC & Wireless

5G/LTE connected PCs, WWAN, Windows drivers, PCIe, MBIM, IMS/VoLTE and hardware/software integration across the OEM ecosystem.

What I owned: Product management · technical leadership · OEM programs · performance and reliability KPIs

  • 100+ design wins
  • ~6 Gbps performance
  • $10M+ recurring revenue