About Paravision Lab
I design, develop, and deploy AI systems end-to-end for rapid prototyping and production.
Solo engineer focused on deterministic orchestration, evaluation, and production deployment.
Representative GenAI product directions I’ve implemented end-to-end, with production constraints in mind.
Each build is shaped around the problem. These are starting points, not templates.
Built a full-stack GenAI research platform that intelligently decides how to answer user queries: via direct LLM response, RAG retrieval pipelines, or external API calls. Implemented agentic routing with LangGraph, real-time streaming, and persistent user sessions in Supabase.
Developed an end-to-end platform generating multi-variant ad scripts with avatar/voice selection, asynchronous video rendering, and persistent asset management. Implemented an event-driven architecture using Inngest for background jobs, retries, and durable execution.
Built a full-stack system orchestrating LangChain-driven scripts + TTS pipelines with non-blocking MP4 rendering via Remotion. Implemented an event-driven workflow where Supabase queues jobs and Remotion workers asynchronously render videos, storing outputs and user assets back in Supabase.
Built an AI-powered platform for YouTube creators to generate thumbnails, analyze competitor content, and optimize SEO workflows. The system intelligently decides how to respond to user queries—direct LLM generation, retrieval from analytics pipelines, or external API calls—ensuring fast, context-aware insights. Implemented persistent user sessions, real-time thumbnail generation, and automated content scoring.
Built an AI branding tool generating logo variations using LangChain prompt pipelines. Implemented an event-driven rendering workflow with Supabase-managed jobs and versioned logo history storage for persistent user sessions.
Developed a voice-first AI interview system with dynamic question generation, real-time voice interviews, automated multi-dimensional scoring, and a responsive user dashboard. Event-driven architecture via Inngest handled background processing, session persistence, and async evaluation workflows.
A structured approach used across product builds, emphasizing deterministic orchestration, system evaluation, and observable AI behavior.
Define the AI workflow, system constraints, user interaction patterns, and evaluation criteria before implementation.
Design orchestration flows, tool interactions, retrieval pipelines, and state management to ensure deterministic and debuggable AI workflows.
Implement AI pipelines with structured outputs, evaluation hooks, retries, and cost/latency controls.
Deploy systems with monitoring, logging, streaming support, and observability hooks for debugging and evaluation.
Designing GenAI systems that address real-world challenges in LLM orchestration, retrieval pipelines, state management, and scalable AI workflows.
Designing modular GenAI systems with clear separation between UI, API, and AI orchestration layers.
Building deterministic agent workflows with explicit control flow and tool routing.
Designing retrieval pipelines using vector search and grounded document retrieval.
Implementing real-time LLM responses using streaming inference and incremental output.
Designing AI workflows that maintain session memory, checkpoints, and conversation state.
Building AI agents that interact with APIs, databases, and external tools.
Designing pipelines that produce validated JSON or schema-based outputs for downstream automation.
Designing multi-step prompt pipelines for reliable reasoning and task execution.
Infrastructure and patterns demonstrated across portfolio builds
Docker · Redis · Inngest · GitHub Actions · LangSmith · Arcjet
Docker and Docker Compose for consistent local and deployed environments
FastAPI-based architecture supporting streaming inference and modular backend components
Inngest for background jobs, retries, and durable execution
Redis and Arcjet for rate limiting and request throttling
Environment secrets and runtime settings for reproducible deployments
Token-based LLM responses with incremental output delivery
Background workers and queue-based systems for long-running AI tasks
GitHub Actions pipelines for automated testing, builds, and deployments
Comprehensive tracing, structured logging, and evaluation via LangSmith
Schema enforcement, input validation, and output verification systems
I'm open to discussing system architecture, engineering trade-offs, and evaluation approaches demonstrated across these builds.
This includes failure modes considered, orchestration patterns chosen, and how the systems behave under async workloads.
